ENERGY OPTIMIZATION IN A MANUFACTURING FACILITY

Description

TECHNICAL FIELD

This disclosure relates to controlling a heating, ventilation, and air conditioning (HVAC) system.

BACKGROUND

Pharmaceutical manufacturing facilities may have an average energy use intensity (EUI) that is much higher than other commercial buildings. The high energy usage of pharmaceutical manufacturing facilities may be caused, in part, by heating, ventilation, and air conditioning (HVAC) systems of the pharmaceutical manufacturing facilities. Such HVAC systems may have to meet stringent requirements for airflow, ventilation and filtration (e.g., air changes per hour ACPH), stringent requirements for environmental conditions (e.g., temperature, humidity, pressure gradients, unidirectional airflow, etc.), which may be needed before, during, and after manufacturing to ensure the quality of the products produced by the facilities, to comply with current good manufacturing practice (cGMP) regulations, and for the safety of personnel working at the facilities.

SUMMARY

In general, aspects of this disclosure are directed to techniques for controlling the heating, ventilation, and air conditioning (HVAC) system of a pharmaceutical manufacturing facility in ways that optimize the energy usage of the HVAC system. An energy optimization system may communicate with a building management system of a pharmaceutical manufacturing facility to control the HVAC system of the facility based on the manufacturing modes of the facility, to thereby reduce the energy usage of the HVAC system without compromising compliance requirements.

In accordance with aspects of this disclosure, an energy optimization system may integrate a schedule of activities and/or the actual state of the activities taking place in a manufacturing facility, and may use the schedule of activities and/or the actual state of the activities to adaptively control the manufacturing facility's HVAC system to adaptively provide different operating conditions for areas of the manufacturing facility in ways that accord with compliance requirements based on the activities taking place in the areas of the manufacturing facility. The energy optimization system may enable users of the facility to input activities into a schedule of activities in an electronic log. Each activity in the schedule of activities may specify or indicate an activity, start and end times for the activity, and the area of the facility where the activity takes place.

The energy optimization system may ingest the schedule of activities in the electronic log and/or the actual state of the activities and may control the facility's HVAC system to provide different operating conditions depending on the activities taking place in the manufacturing facility. The energy optimization system may, for an activity taking place in an area of the manufacturing facility, determine the operational mode associated with the activity. The energy optimization system may, based on the operational mode associated with the activity, control the portion of the HVAC system associated with the area, such as one or more air handling units that regulate and circulate air for the area, to provide and maintain the proper operating conditions for the activity from the start of the activity until the end of the activity.

The techniques of this disclosure may provide certain technical advantages. By controlling a manufacturing facility's HVAC system to adaptively provide different operating conditions for areas of the manufacturing facility based on the activities taking place in the areas of the manufacturing facility, thereby reducing energy usage of the manufacturing facility. Further, by integrating a schedule of activities taking place in a manufacturing facility with the energy optimization system, the energy optimization system may be able to determine, in near real time, the activities are occurring in the areas of the manufacturing facility, which enables the energy optimization system to adaptively provide different operating conditions for areas of the manufacturing facility based on the activities taking place in the areas of the manufacturing facility.

In some aspects, the techniques described herein relate to a computer-implemented method including: determining, by a computing system, an upcoming event associated with an activity associated with an area of a manufacturing facility out of a plurality of areas of a manufacturing facility; identifying, by the computing system, one or more air handling units (AHUs) associated with the area of the manufacturing facility out of a plurality of AHUs associated with the manufacturing facility; determining, by the computing system and based on the activity, an operational mode for the one or more AHUs out of a plurality of operational modes; and sending, by the computing system and to a building management system, instructions that cause the one or more AHUs to operate according to the operational mode.

In some aspects, the techniques described herein relate to a computing system including: memory; and one or more processors communicably coupled to the memory and configured to: determine an upcoming event associated with an activity associated with an area of a manufacturing facility out of a plurality of areas of a manufacturing facility; identify one or more air handling units (AHUs) associated with the area of the manufacturing facility out of a plurality of AHUs associated with the manufacturing facility; determine, based on the activity, an operational mode for the one or more AHUs out of a plurality of operational modes; and send, to a building management system, instructions that cause the one or more AHUs to operate according to the operational mode.

In some aspects, the techniques described herein relate to a non-transitory computer-readable storage medium including instructions, that when executed by one or more processors of a computing system, cause the one or more processors to: determine an upcoming event associated with an activity associated with an area of a manufacturing facility out of a plurality of areas of a manufacturing facility; identify one or more air handling units (AHUs) associated with the area of the manufacturing facility out of a plurality of AHUs associated with the manufacturing facility; determine, based on the activity, an operational mode for the one or more AHUs out of a plurality of operational modes; and send, to a building management system, instructions that cause the one or more AHUs to operate according to the operational mode.

The details of one or more examples are set out in the accompanying drawings and the description below. Other features, objects, and advantages will be apparent from the description and drawings, and from the claims.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 shows a system for energy optimization, in accordance with one or more aspects of the present disclosure.

FIG. 2 is a block diagram illustrating an example computing system, in accordance with one or more aspects of the present disclosure.

FIG. 3A illustrates an example mapping of HVAC assets to areas of a manufacturing facility, in accordance with one or more aspects of the present disclosure.

FIG. 3B illustrates an example mapping of manufacturing equipment to areas of a manufacturing facility, in accordance with one or more aspects of the present disclosure.

FIG. 3C illustrates an example mapping of site-specific operational modes to core operational modes, in accordance with one or more aspects of the present disclosure.

FIG. 3D illustrates example operational parameters for core operational modes, in accordance with one or more aspects of the present disclosure.

FIG. 3E illustrates example hierarchies of parent air handling units (AHUs) and child AHUs, in accordance with one or more aspects of the present disclosure.

FIG. 4 illustrates an example of an electronic log, in accordance with one or more aspects of the present disclosure.

FIGS. 5A-5D illustrate example user interfaces presented by an example energy optimization system, in accordance with one or more aspects of the present disclosure.

FIG. 6. is a flowchart illustrating example operations performed by an example energy optimization system, in accordance with one or more aspects of the present disclosure.

DETAILED DESCRIPTION

In general, aspects of this disclosure are directed to techniques for controlling the heating, ventilation, and air conditioning (HVAC) system of a manufacturing facility in ways that optimize the energy usage of the HVAC system. An energy optimization system may communicate with a building management system of the manufacturing facility to control the HVAC system of the facility based on the manufacturing modes of the facility, to thereby reduce the energy usage of the HVAC system. The techniques of this disclosure may be of particular benefit to manufacturing facilities, such as pharmaceutical manufacturing facilities, that must operate under stringent temperature and air quality constraints.

A pharmaceutical manufacturing facility may operate at a capacity utilization rate of 30-35%, which is in contrast to the 80%-85% capacity utilization rate of manufacturing facilities in the consumer products industry. That is, a pharmaceutical manufacturing facility may actively be manufacturing pharmaceuticals for 30-35% of the time. Such a low capacity utilization rate may be due to the amount of time that is spent cleaning the facility before and after manufacturing, changeover downtime, which is capacity lost during changes to material, equipment, and product, setup downtime for equipment adjustments, unexpected equipment failures, process failures (e.g., due to defective materials, operating errors, leaks, spills, etc.), and fluctuations in supply and demand for materials. The low capacity utilization rate of a pharmaceutical manufacturing facility may also be caused by production adjustments, delays caused by time waiting for regulatory inspections and approvals, time spent for preventative maintenance, including periodic calibration and requalification of manufacturing equipment, breaks, training time, and other production stops.

However, some pharmaceutical manufacturing facilities continuously operate their HVAC systems as if the facilities are in operation to actively manufacture pharmaceuticals. That is, the HVAC systems may continuously operate, 24/7, to meet the stringent airflow, ventilation, filtration, and environmental conditions requirements for active manufacture of pharmaceuticals, regardless of whether the facilities are actively manufacturing pharmaceutical products. Such continuous operation of HVAC systems to meet the requirements for active manufacture of pharmaceuticals may cause pharmaceutical manufacturing facilities to have much higher average energy use intensity (EUI) compared to other commercial buildings and other types of manufacturing plants.

In accordance with aspects of this disclosure, an energy optimization system may synchronize the operations of a HVAC system of a pharmaceutical manufacturing facility with manufacturing operations of the pharmaceutical manufacturing facility, to thereby align the operations of the HVAC system with the manufacturing modes of the facility.

Areas of a pharmaceutical manufacturing facility may operate in different operational modes. Each operational mode for an area of the facility may be associated with an activity that is taking place in the area of the facility. For example, an area of a pharmaceutical facility may operate in a “in operation” mode while actively manufacturing pharmaceutical products but may operate in an “at rest” mode when the area is being prepped to be ready for, but is not yet in the process of, actively manufacturing pharmaceutical products.

Different operational modes of an area may require different operating conditions. An area of a facility, when actively manufacturing pharmaceutical products, may require the strictest of operating conditions, while an area of the facility, when manufacturing equipment is being maintained or when being prepped for actively manufacturing pharmaceutical product, may require less strict operating conditions. The level of strictness of operating conditions of an area of the facility may correspond to the among of energy usage of the facility's HVAC system to maintain the operating conditions. Maintaining the strictest of operating conditions for an area that is actively manufacturing pharmaceutical products may use more energy than maintaining less strict levels of operating conditions for the same area.

In accordance with aspects of this disclosure, an energy optimization system may integrate a schedule of activities taking place in a manufacturing facility and use the schedule of activities to adaptively control the manufacturing facility's HVAC system to adaptively provide different operating conditions for areas of the manufacturing facility based on the activities taking place in the areas of the manufacturing facility. The energy optimization system may enable users of the facility to input activities into a schedule of activities in an electronic log. Each activity in the schedule of activities may specify or indicate an activity, start and end times for the activity, and the area of the facility where the activity takes place.

The energy optimization system may ingest the schedule of activities in the electronic log and may control the facility's HVAC system to provide different operating conditions depending on the activities taking place in the manufacturing facility. The energy optimization system may, for an activity taking place in an area of the manufacturing facility, determine the operational mode associated with the activity. The energy optimization system may, based on the operational mode associated with the activity, control the portion of the HVAC system associated with the area, such as one or more air handling units that regulate and circulate air for the area, to provide and maintain the proper operating conditions for the activity from the start of the activity until the end of the activity. For example, the energy optimization system may, for actively manufacturing pharmaceutical products in an area of the facility, control the portion of the HVAC system associated with the area to provide the strictest level of operating conditions, and may, for other activities in the area, control the portion of the HVAC system associated with the area to provide less strict levels of operating conditions.

The techniques of this disclosure may provide certain technical advantages. By controlling a manufacturing facility's HVAC system to adaptively provide different operating conditions for areas of the manufacturing facility based on the activities taking place in the areas of the manufacturing facility, thereby reducing energy usage of the manufacturing facility. Further, by integrating a schedule of activities taking place in a manufacturing facility with the energy optimization system, the energy optimization system may be able to determine, in near real time, the activities are occurring in the areas of the manufacturing facility, which enables the energy optimization system to adaptively provide different operating conditions for areas of the manufacturing facility based on the activities taking place in the areas of the manufacturing facility.

FIG. 1 shows a system for energy optimization, in accordance with one or more aspects of the present disclosure. In the example of FIG. 1, energy optimization system 102 may communicate with building management system 150 to control air handling units (AHUs) 172A-172N (“air handling units 172”) of manufacturing facility 170. Energy optimization system 102 and building management system 150 may communicate with data system 140 (e.g., over a network) to retrieve data used to perform energy optimization of manufacturing facility 170.

Data system 140 includes events data store 142 that stores electronic log 144. Electronic log 144 stores a schedule of activities in manufacturing facility 170. Each activity in the schedule of events may be associated with an area (e.g., a room, a space, a zone, etc.) in manufacturing facility 170, out of a plurality of areas 174A-174K (“areas 174”) in manufacturing facility 170, and/or associated with particular pieces of manufacturing equipment in manufacturing facility 170. Each activity in the schedule of activities may also have an associated start time and end time. Users may interact with data system 140 to modify the schedule of activities in electronic log 144, such as by adding new activities to the schedule of events, rescheduling activities, and/or removing activities.

Data system 140 may represent any suitable computing system, such as one or more desktop computers, laptop computers, mainframes, servers, cloud computing systems, etc. capable of sending and receiving information both to and from a network. In some examples, data system 140 may represent cloud computing systems that provide access to their respective services via a cloud. Events data store 142 may be any suitable data store or repository, such as a database, that is configured to store electronic log 144 and other data associated with controlling AHUs 172. Examples of electronic log 144 may include text files, spreadsheets, database tables, comma separated values, structured or unstructured data, and the like. In some examples, events data store 142 may also store operational data 146 for manufacturing facility 170, such as data related to commissioning and qualification of AHUs 172, such as data related to expected area conditions in different operational modes, corresponding operational parameters of AHUs 172, ranges, set points, and the like.

Building management system 150 is configured to control the operations of air handling units (AHUs) 172A-172N (collectively “AHUs 172”) within manufacturing facility 170. For example, building management system 150 may send instructions, such as control signals, to AHUs 172 to control the operational parameters of AHUs 172. Building management system 150 may represent any suitable computing system, such as one or more desktop computers, laptop computers, mainframes, servers, cloud computing systems, etc. capable of sending and receiving information both to and from a network. In some examples, building management system 150 may represent cloud computing systems that provide access to their respective services via a cloud.

Each of AHUs 172 is a device that regulates and circulates air for an associated area of manufacturing facility 170 out of areas 174, to purify, air-condition, and/or renewing the indoor air for the associated area of manufacturing facility 170. AHUs 172 is part of manufacturing facility 170's heating, ventilation, and air conditioning (HVAC) system. A chilled water system such as a chilled water plant (not shown) may supply chilled water to AHUs 172. Similarly, a hot water system such as a boiler (not shown) may supply hot water to AHUs 172. AHUs 172 may use the chilled water and hot water to control the temperature and humidity within associated areas 174 of manufacturing facility. AHUs 172 may also be connected to electric power (not shown) to power AHUs 172.

Energy optimization system 102 is configured to communicate with building management system 150 to adaptively control AHUs 172 based on events associated with manufacturing facility 170. Energy optimization system 102 may represent any suitable computing system, such as one or more desktop computers, laptop computers, mainframes, servers, cloud computing systems, etc. capable of sending and receiving information both to and from a network. In some examples, energy optimization system 102 may represent cloud computing systems that provide access to their respective services via a cloud.

Energy optimization system 102 includes manufacturing facility model 104, optimization engine 106, portal interface 108, and configuration data store 110. Configuration data store 110 may be any suitable data store or repository, such as a database. Manufacturing facility model 104, optimization engine 106, and portal interface 108 may perform operations described herein using hardware, software, firmware, or a mixture thereof residing in and/or executing at energy optimization system 102. Energy optimization system 102 may execute manufacturing facility model 104, optimization engine 106, and portal interface 108 with one processor or with multiple processors. In some examples, energy optimization system 102 may execute manufacturing facility model 104, optimization engine 106, and portal interface 108 as a virtual machine executing on underlying hardware. Manufacturing facility model 104, optimization engine 106, and portal interface 108 may execute as one or more services of an operating system or computing platform or may execute as one or more executable programs at an application layer of a computing platform.

Configuration data store 110 may store mappings of manufacturing equipment in manufacturing facility 170 to areas 174 of manufacturing facility 170. That is, configuration data store 110 may store a mapping of each piece of manufacturing equipment in manufacturing facility 170 to an specific area of manufacturing facility 170, and may store a mapping of each area of manufacturing facility 170 to one or more pieces of manufacturing equipment in the area.

Configuration data store 110 may also store a mapping of HVAC assets of manufacturing facility 170, such as AHUs 172, to areas 174 of manufacturing facility 170. That is, configuration data store 110 may, for each AHU of AHUs 172, map the AHU to a specific area of manufacturing facility 170, and may, for each area of manufacturing facility 170, map one or more of AHUs 172 to the area.

Configuration data store 110 may also store a mapping of site-specific operational modes to core operational modes. Each activity in manufacturing facility 170 may be associated with an operational mode that is indicative of the activity. For example, electronic log 144 may store, for each activity in the schedule of activities, an associated operational mode. In some examples, electronic log 144 may associate site-specific operational modes, which are operational modes specific to manufacturing facility 170, and configuration data store 110 may store a mapping of such site-specific operational modes to core operational modes, which are operational modes generalized across different manufacturing facilities.

In some examples, the core operational modes include an in operation mode, a predictive maintenance and calibration (PMC) mode, an at rest mode, and an idle mode. The in operation mode is associated with manufacturing activities, such as manufacturing of pharmaceutical products, that are taking place. Examples of such manufacturing activities include mixing, granulation, tablet compression, coating, and packaging. The in operation mode may be associated with strict operating conditions, such as with respect to air changes, temperature, humidity, air pressure, and the like for the area in manufacturing facility 170 where the activity is taking place.

The PMC mode is associated with activities related to performing maintenance and calibration of manufacturing equipment in manufacturing facility 170. The PMC mode may be associated with relatively less strict operating conditions for the area in manufacturing facility 170 where the activity is taking place compared with the in operation mode.

The at rest mode is associated with activities related to preparing an area of the manufacturing facility 170 for manufacturing activities. The at rest mode may be associated with a minimal level of operating conditions for the area in manufacturing facility 170.

The idle mode may be enabled when there is no active production or other activities in an area of manufacturing facility 170. In the idle mode, AHUs 172 in the area can be shut down or may operate at a reduced frequency to support differential pressure needs of other areas of manufacturing facility 170.

Configuration data store 110 may also store, for each core operational mode, associated operational parameters of AHUs 172. The operating parameter associated with an operational mode may be designed to achieve one or more particular operating conditions for an area of manufacturing facility 170 given the operational mode. The one or more particular operating conditions may include whether a given AHU in the area is turned on or turned off, the blower speed of the AHU in the area, the temperature in the area, the relative humidity of the area, the differential pressure of the area, and the like. In some examples, such operating parameters can be stored in data system 140.

Configuration data store 110 may also store information regarding the hierarchy of AHUs 172 in manufacturing facility. AHUs 172 may include parent AHUs and child AHUs, and configuration data store 110 may store information regarding which AHUs of AHUs 172 are parent AHUs and which AHUs of AHUs 172 are child AHUs of the parent AHUs.

A parent AHU may be a relatively larger central unit responsible for conditioning and distributing air to multiple zones or area within manufacturing facility 170, and a parent AHU may supply air to one or more child AHUs of the parent AHU.

A child AHU may service a single zone or area within manufacturing facility 170. A child AHU may be a secondary, relatively smaller unit that receives air from a parent AHU to which it is connected. A child AHU may further adjust or condition the air supplied by its parent AHU to meet the specific requirements of their designated zone or area. For instance, a child AHU may fine-tune temperature, humidity, or filtration levels based on local needs, such as cleanrooms, specific manufacturing processes or lab spaces, for the area associated with the child AHU.

Manufacturing facility model 104 may be a digital twin of manufacturing facility 170. Manufacturing facility model 104 may receive real-time data from manufacturing facility 170 (e.g., sensor data from sensors in manufacturing facility 170) to mirror the state of manufacturing facility 170 at a given moment. Manufacturing facility model 104 may model the operations of manufacturing facility 170, including modeling operations of manufacturing equipment in manufacturing facility 170, the state of areas 174 of manufacturing facility 170, the operations of AHUs 172, and the like. In some examples, manufacturing facility model 104 may receive, from sensors in manufacturing facility 170, sensor data such as temperatures of areas 174 of manufacturing facility 170, relative humidities of areas 174 of manufacturing facility 170, differential pressures of areas 174 of manufacturing facility 170, the statuses of AHUs 172, the blower speeds of AHUs 172, chilled water control valve data, hot water control valve data, air changes per hour, and the like.

Optimization engine 106 is configured to determine events occurring within manufacturing facility 170 and to communicate with building management system 150 to control operations of AHUs to optimize energy usage of manufacturing facility 170 based on the events. Optimization engine 106 may integrates data system 140 into energy optimization system 102 and ingest, from data system 140, data stored in events data store 142, such as the information contained in electronic log 144. For example, optimization engine 106 may ingest the schedule of activities in manufacturing facility 170 as specified in electronic log 144 to determine upcoming events in manufacturing facility 170 for which energy optimization system 102 may control the operations of AHUs 172.

Optimization engine 106 may, based on ingesting the schedule of activities in electronic log 144, generate events associated with the activities in the schedule of activities. Optimization engine 106 may, for an activity in the schedule of activities, generate one or more events. Each event may be associated with an activity in the schedule of activities, a start time of the activity, an area of manufacturing facility 170 where the activity is taking place, and/or manufacturing equipment within the manufacturing facility 170 used to perform the activity. Each activity in the schedule of activities may be an operational mode, such as a site-specific operational mode or a core operational mode. As the schedule of activities in electronic log 144 is updated to add, remove, and modify activities, optimization engine 106 may process the activities in near real-time to generate events based on the schedule of activities and to process the generated events.

In some examples, given an activity associated with a start time and an end time, optimization engine 106 may generate two events: a first event associated with the start of the activity and a second event associated with the end of the activity. Optimization engine 106 may also generate one or more events based on an absence of scheduled activities within time periods. For example, optimization engine 106 may determine, based on the schedule of activities, that there is no activity for an area of manufacturing facility 170 for a time period that exceeds a specified threshold (e.g., 30 minutes). Optimization engine 106 may, in response generate an event associated with an idle mode for the area of manufacturing facility 170 during that time period.

Optimization engine 106 may, for each of a plurality of areas 174 of manufacturing facility 170 control the operations of AHUs 172 associated with the area based on the generated events. To determine events associated with a specific area of manufacturing facility 170, optimization engine 106 may filter the generated events for events associated with a specific area. In some examples, the events may specify an associated area where an associated activity is taking place. In some examples, if the events specify manufacturing equipment associated with each of the generated events, optimization engine 106 may use the mappings of manufacturing equipment in manufacturing facility 170 to areas 174 of manufacturing facility 170 stored in configuration data store 110 to filter the events for events associated with manufacturing equipment in the specific area of manufacturing facility 170.

Optimization engine 106 may also determine, for each of a plurality of areas 174 of manufacturing facility 170, a corresponding one or more AHUs out of AHUs 172 associated with the area. For example, optimization engine 106 may use the mapping of AHUs to areas 174 of manufacturing facility 170 stored in configuration data store 110 to determine, for each of a plurality of areas 174 of manufacturing facility 170, a corresponding one or more AHUs out of AHUs 172 associated with the area. The one or more AHUs associated with an area may be one or more AHUs that regulates and circulates air for the area. The one or more AHUs may include one or more child AHUs that serve the area and one or more parent AHUs that distributes air to the one or more child AHUs.

Optimization engine 106 may generate events associated with activities in electronic log 144 in order according to the order of the activities in electronic log 144, and optimization engine 106 may, for events associated with an area of manufacturing facility 170, process the events in a first in, last out order, based on the ordering specified in the schedule of events.

Optimization engine 106 may, for an upcoming event in an area of manufacturing facility 170, determine, based on the activity associated with the event, an operational mode for one or more AHUs associated with the area out of a plurality of operational modes. For example, if the activity is a site specific operational mode, optimization engine 106 may map, based on the mapping of site specific operational modes to core operational modes stored in configuration data store 110, the activity to a core operational mode.

Optimization engine 106 may prioritize operational modes associated with an area. For example, events associated with the same area of manufacturing facility 170 but with different activities (and therefore different operational modes) may overlap in time. As such, optimization engine 106 may prioritize the events according to the associated core operational modes of the events. Optimization engine 106 may prioritize an event associated with an in operation mode over an event associated with a PMC mode, prioritize an event associated with a PMC mode over an event associated with an at rest mode, and may prioritize an event associated with an at rest mode over an event associated with an idle mode.

Optimization engine 106 may send, to building management system 150, instructions that cause the one or more AHUs associated with the area to operate according to the operational mode. That is, optimization engine 106 may, for an event associated with an activity in a specific area of manufacturing facility 170, control one or more AHUs associated with the specific area to provide operating conditions that correspond to the operational mode for the activity by the start time of the activity.

The instructions sent by optimization engine 106 may include instructions that cause the one or more AHUs to operate according to certain operational parameters. As described above, each operational mode may be associated with operational parameters, such as whether the one or more AHUs are turned on or off, the blower speeds of the one or more AHUs, the temperature of the associated area, the relative humidity of the associated area, the differential pressure of the area, and the like. As such, the instructions sent by optimization engine 106 may include instructions that causes the one or more AHUs to achieve the operational parameters associated with the determined operational mode for the event.

As described above, the one or more AHUs associated with an area of manufacturing facility 170 may include one or more parent AHUs and one or more child AHUs. Optimization engine 106 may determine a sequencing of the one or more parent AHUs and one or more child AHUs associated with the area to operate according to the determined operational mode for the event, and may include, in the instructions sent by optimization engine 106, instructions that cause the one or more parent AHUs and one or more child AHUs associated with the area to sequentially operate according to the operational mode based on the sequencing of the one or more AHUs and the one or more parent AHUs. For example, the instructions sent by optimization engine 106 may include instructions to start the one or more parent AHUs prior to starting the one or more child AHUs, or to shut off the one or more child AHUs prior to shutting off the one or more parent AHUs.

Building management system 150 may receive the instructions sent by optimization engine 106 that cause one or more AHUs associated with an area of manufacturing facility 170 to operate according to an operational mode. Building management system 150 may, based on the instructions, send corresponding control signals to the one or more AHUs associated with the area to cause the one or more AHUs to operate according to an operational mode. Building management system 150 may use a proportional-integral-derivative (PID) controller 152 to perform continuous modulated control of the one or more AHUs to cause the area of manufacturing facility 170 to provide operating conditions that correspond to the operational mode for the activity.

Energy optimization system 102 also includes portal interface 108. Energy optimization system 102 may communicate with computing devices 130A-130N (collectively “computing devices 130”), and portal interface 108 may present information, such as in the form of dashboards, regarding energy usage and energy savings of manufacturing facility 170 to computing devices 130.

FIG. 2 is a block diagram illustrating an example computing system, in accordance with one or more aspects of the present disclosure. Computing system 200 of FIG. 2 is described below as an example of energy optimization system 102 of FIG. 1. FIG. 2 illustrates only one particular example of computing system 200, and many other examples of computing system 200 may be used in other instances and may include a subset of the components included in example computing system 200 or may include additional components not shown in FIG. 2. For example, computing system 200 may comprise a cluster of servers, and each of the servers comprising the cluster of servers making up computing system 200 may include all, or some, of the components described herein in FIG. 2, to perform the techniques disclosed herein.

As shown in the example of FIG. 2, computing system 200 includes one or more processors 240, one or more communication units 244, and one or more storage components 248. Storage components 248 include manufacturing facility model 204, which is an example of manufacturing facility model 104 of FIG. 1, optimization engine 206, which is an example of optimization engine 106 of FIG. 1, portal interface 208, which is an example of portal interface 108 of FIG. 1, and configuration data store 210, which is an example of configuration data store 110 of FIG. 1.

One or more processors 240 may implement functionality and/or execute instructions associated with computing system 200. Examples of one or more processors 240 include application processors, display controllers, auxiliary processors, one or more sensor hubs, and any other hardware configure to function as a processor, a processing unit, or a processing device. Manufacturing facility model 204, optimization engine 206, and portal interface 208 may be operable by one or more processors 240 to perform various actions, operations, or functions of computing system 200. For example, one or more processors 240 of computing system 200 may retrieve and execute instructions stored by one or more storage components 248 that cause one or more processors 240 to perform the operations of manufacturing facility model 204, optimization engine 206, and portal interface 208. The instructions, when executed by one or more processors 240, may cause computing system 200 to store information within one or more storage components 248, for example, in configuration data store 210.

One or more communication units 244 of computing system 200 may communicate with external devices (e.g., data system 140 and building management system 150 of FIG. 1) via one or more wired and/or wireless networks by transmitting and/or receiving network signals on the one or more networks. Examples of one or more communication units 244 include a network interface card (e.g., such as an Ethernet card), an optical transceiver, a radio frequency transceiver, a global positioning satellite (GPS) receiver, or any other type of device that can send and/or receive information. Other examples of one or more communication units 244 may include short wave radios, cellular data radios, wireless network radios, as well as universal serial bus (USB) controllers.

Communication channels 251 may interconnect each of the components 240, 242, and 248 for inter-component communications (physically, communicatively, and/or operatively). In some examples, communication channels 251 may include a system bus, a network connection, an inter-process communication data structure, or any other method for communicating data.

One or more storage components 248 within computing system 200 may store information for processing during operation of computing system 200 (e.g., computing system 200 may store data accessed by manufacturing facility model 204, optimization engine 206, and portal interface 208 during execution at computing system 200). For example, one or more storage components 248 may store configuration data store 210. In some examples, one or more storage components 248 is a temporary memory, meaning that a primary purpose of one or more storage components 248 is not long-term storage. In this example, one or more storage components 248 may be configured for short-term storage of information as volatile memory and therefore not retain stored contents if powered off. Examples of volatile memories include random access memories (RAM), dynamic random access memories (DRAM), static random access memories (SRAM), and other forms of volatile memories known in the art.

In some examples, one or more storage components 248 may also include one or more computer-readable storage media. One or more storage components 248, in some examples, include one or more non-transitory computer-readable storage mediums. One or more storage components 248 may be configured to store larger amounts of information than typically stored by volatile memory. One or more storage components 248 may further be configured for long-term storage of information as non-volatile memory space and retain information after power on/off cycles. Examples of non-volatile memories include magnetic hard discs, optical discs, floppy discs, flash memories, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable (EEPROM) memories. One or more storage components 248 may store program instructions and/or information (e.g., data) associated with manufacturing facility model 204, optimization engine 206, and portal interface 208. Storage components 248 may include a memory configured to store data or other information associated with manufacturing facility model 204, optimization engine 206, and portal interface 208, and configuration data store 210.

Configuration data store 210 is configured to store mappings of manufacturing equipment in a manufacturing facility (e.g., manufacturing facility 170 of FIG. 1) to areas (e.g., areas 174) of the manufacturing facility. That is, configuration data store 210 may store a mapping of each piece of manufacturing equipment in manufacturing facility 170 to an specific area of manufacturing facility 170, and may store a mapping of each area of manufacturing facility 170 to one or more pieces of manufacturing equipment in the area.

Configuration data store 210 is also configured to store a mapping of HVAC assets of manufacturing facility 170, such as AHUs 172, to areas 174 of manufacturing facility 170. That is, configuration data store 210 may, for each AHU of AHUs 172, map the AHU to a specific area of manufacturing facility 170, and may, for each area of manufacturing facility 170, map one or more of AHUs 172 to the area.

Configuration data store 210 is also configured to store a mapping of site-specific operational modes to core operational modes. Each activity in manufacturing facility 170 may be associated with an operational mode that is indicative of the activity. For example, electronic log 144 may store, for each activity in the schedule of activities, an associated operational mode. In some examples, electronic log 144 may associate site-specific operational modes, which are operational modes specific to manufacturing facility 170, and configuration data store 110 may store a mapping of such site-specific operational modes to core operational modes, which are operational modes generalized across different manufacturing facilities.

Configuration data store 110 is also configured to store, for each core operational mode, associated operational parameters of AHUs 172. The operating parameter associated with an operational mode may be designed to achieve one or more particular operating conditions for an area of manufacturing facility 170 given the operational mode. The one or more particular operating conditions may include whether a given AHU in the area is turned on or turned off, the blower speed of the AHU in the area, the temperature in the area, the relative humidity of the area, the differential pressure of the area, and the like.

Configuration data store 110 is also configured to store information regarding the hierarchy of AHUs 172 in manufacturing facility. AHUs 172 may include parent AHUs and child AHUs, and configuration data store 110 may store information regarding which AHUs of AHUs 172 are parent AHUs and which AHUs of AHUs 172 are child AHUs of the parent AHUs.

A parent AHU may be a relatively larger central unit responsible for conditioning and distributing air to multiple zones or area within manufacturing facility 170, and a parent AHU may supply air to one or more child AHUs of the parent AHU.

A child AHU may service a single zone or area within manufacturing facility 170. A child AHU may be a secondary, relatively smaller unit that receives air from a parent AHU to which it is connected. A child AHU may further adjust or condition the air supplied by its parent AHU to meet the specific requirements of their designated zone or area. For instance, a child AHU may fine-tune temperature, humidity, or filtration levels based on local needs, such as cleanrooms, specific manufacturing processes or lab spaces, for the area associated with the child AHU.

Manufacturing facility model 204 may be a digital twin of manufacturing facility 170. One or more processors 240 are configured to execute manufacturing facility model 204 to receive real-time data from manufacturing facility 170 (e.g., sensor data from sensors in manufacturing facility 170) to mirror the state of manufacturing facility 170 at a given moment. For example, manufacturing facility model 204 may receive, from sensors in manufacturing facility 170, sensor data such as temperatures of areas 174 of manufacturing facility 170, relative humidities of areas 174 of manufacturing facility 170, differential pressures of areas 174 of manufacturing facility 170, the statuses of AHUs 172, the blower speeds of AHUs 172, chilled water control valve data, hot water control valve data, air changes per hour, and the like.

Other components and modules of computing system 200, such as optimization engine 206, may use manufacturing facility model 204 to perform real-time monitoring, analysis, and optimization of the operations of AHUs 172 in manufacturing facility 170. For example, other components and modules of computing system 200 may use manufacturing facility model 204 to simulate outcomes from real-time conditions of manufacturing facility 170, which may enable “what if” analyses across different operational scenarios of manufacturing facility 170.

One or more processors 240 are configured to execute optimization engine 206 to determine events occurring within manufacturing facility 170 and to communicate with building management system 150 to control operations of AHUs to optimize energy usage of manufacturing facility 170 based on the events. Optimization engine 206 may integrates data system 140 into computing system 200 and ingest, from data system 140, data stored in events data store 142, such as the information contained in electronic log 144. For example, optimization engine 206 may ingest the schedule of activities in manufacturing facility 170 as specified in electronic log 144 to determine upcoming events in manufacturing facility 170 for which energy optimization system 102 may control the operations of AHUs 172.

Optimization engine 206 may, based on ingesting the schedule of activities in electronic log 144, generate events associated with the activities in the schedule of activities. Optimization engine 206 may, for an activity in the schedule of activities, generate one or more events. Each event may be associated with an activity in the schedule of activities, a start time of the activity, an area of manufacturing facility 170 where the activity is taking place, and/or manufacturing equipment within the manufacturing facility 170 used to perform the activity. Each activity in the schedule of activities may be an operational mode, such as a site-specific operational mode or a core operational mode. As the schedule of activities in electronic log 144 is updated to add, remove, and modify activities, optimization engine 106 may process the activities in near real-time to generate events based on the schedule of activities and to process the generated events.

In some examples, given an activity associated with a start time and an end time, optimization engine 206 may generate two events: a first event associated with the start of the activity and a second event associated with the end of the activity. Optimization engine 206 may also generate one or more events based on an absence of scheduled activities within time periods. For example, optimization engine 206 may determine, based on the schedule of activities, that there is no activity for an area of manufacturing facility 170 for a time period that exceeds a specified threshold (e.g., 30 minutes). Optimization engine 206 may, in response generate an event associated with an idle mode for the area of manufacturing facility 170 during that time period.

Optimization engine 206 may, for each of a plurality of areas 174 of manufacturing facility 170 control the operations of AHUs 172 associated with the area based on the generated events. To determine events associated with a specific area of manufacturing facility 170, optimization engine 206 may filter the generated events for events associated with a specific area. In some examples, the events may specify an associated area where an associated activity is taking place. In some examples, if the events specify manufacturing equipment associated with each of the generated events, optimization engine 206 may use the mappings of manufacturing equipment in manufacturing facility 170 to areas 174 of manufacturing facility 170 stored in configuration data store 210 to filter the events for events associated with manufacturing equipment in the specific area of manufacturing facility 170.

Optimization engine 206 may also determine, for each of a plurality of areas 174 of manufacturing facility 170, a corresponding one or more AHUs out of AHUs 172 associated with the area. For example, optimization engine 206 may use the mapping of AHUs to areas 174 of manufacturing facility 170 stored in configuration data store 210 to determine, for each of a plurality of areas 174 of manufacturing facility 170, a corresponding one or more AHUs out of AHUs 172 associated with the area. The one or more AHUs associated with an area may be one or more AHUs that regulates and circulates air for the area. The one or more AHUs may include one or more child AHUs that serve the area and one or more parent AHUs that distributes air to the one or more child AHUs.

Optimization engine 206 may generate events associated with activities in electronic log 144 in order according to the order of the activities in electronic log 144, and optimization engine 206 may, for events associated with an area of manufacturing facility 170, process the events in a first in, last out order, based on the ordering specified in the schedule of events.

Optimization engine 206 may, for an upcoming event in an area of manufacturing facility 170, determine, based on the activity associated with the event, an operational mode for one or more AHUs associated with the area out of a plurality of operational modes. For example, if the activity is a site specific operational mode, optimization engine 206 may map, based on the mapping of site specific operational modes to core operational modes stored in configuration data store 210, the activity to a core operational mode.

Optimization engine 206 may prioritize operational modes associated with an area. For example, events associated with the same area of manufacturing facility 170 but with different activities (and therefore different operational modes) may overlap in time. As such, optimization engine 206 may prioritize the events according to the associated core operational modes of the events. Optimization engine 206 may prioritize an event associated with an in operation mode over an event associated with a PMC mode, prioritize an event associated with a PMC mode over an event associated with an at rest mode, and may prioritize an event associated with an at rest mode over an event associated with an idle mode.

Optimization engine 206 may send, to building management system 150, instructions that cause the one or more AHUs associated with the area to operate according to the operational mode. That is, optimization engine 206 may, for an event associated with an activity in a specific area of manufacturing facility 170, control one or more AHUs associated with the specific area to provide operating conditions that correspond to the operational mode for the activity by the start time of the activity.

The instructions sent by optimization engine 206 may include instructions that cause the one or more AHUs to operate according to certain operational parameters. As described above, each operational mode may be associated with operational parameters, such as whether the one or more AHUs are turned on or off, the blower speeds of the one or more AHUs, the temperature of the associated area, the relative humidity of the associated area, the differential pressure of the area, and the like. As such, the instructions sent by optimization engine 206 may include instructions that causes the one or more AHUs to achieve the operational parameters associated with the determined operational mode for the event.

Optimization engine 206 may use manufacturing facility model 204 to determine the instructions to cause the one or more AHUs to achieve the operational parameters associated with the operational mode. Because manufacturing facility model 204 is a digital twin of manufacturing facility 170 that performs real-time mirroring of the current state of manufacturing facility 170, optimization engine 206 may send, to manufacturing facility model 204, various instructions for controlling one or more AHUs associated with the specific area to determine whether the instructions would cause the AHUs to achieve the operational parameters associated with the operational mode. For example, optimization engine 206 may send various instructions to manufacturing facility model 204 to control one or more AHUs associated with the specific area and may, in response, receive feedback from manufacturing facility model 204 regarding whether the one or more AHUs achieved the operational parameters associated with the operational mode (e.g., room temperature, relative humidity, differential pressure, etc.) to determine a set of instructions for the one or more AHUs associated with the specific area. Optimization engine 206 may, in response to determining, from its interactions with manufacturing facility model 204, that a set of instructions for controlling the one or more AHUs associated with the specific area achieves the operational parameters associated with the operating mode, send, to building management system 150, the set of instructions that cause the one or more AHUs associated with the area to operate according to the operational mode.

As described above, the one or more AHUs associated with an area of manufacturing facility 170 may include one or more parent AHUs and one or more child AHUs. Optimization engine 206 may determine a sequencing of the one or more parent AHUs and one or more child AHUs associated with the area to operate according to the determined operational mode for the event, and may include, in the instructions sent by optimization engine 206, instructions that cause the one or more parent AHUs and one or more child AHUs associated with the area to sequentially operate according to the operational mode based on the sequencing of the one or more AHUs and the one or more parent AHUs. For example, the instructions sent by optimization engine 206 may include instructions to start the one or more parent AHUs prior to starting the one or more child AHUs, or to shut off the one or more child AHUs prior to shutting off the one or more parent AHUs.

Building management system 150 may receive the instructions sent by optimization engine 206 that cause one or more AHUs associated with an area of manufacturing facility 170 to operate according to an operational mode. Building management system 150 may, based on the instructions, send corresponding control signals to the one or more AHUs associated with the area to cause the one or more AHUs to operate according to an operational mode. Building management system 150 may use a proportional-integral-derivative (PID) controller 152 to perform continuous modulated control of the one or more AHUs to cause the area of manufacturing facility 170 to provide operating conditions that correspond to the operational mode for the activity.

One or more processors 240 is configured to execute portal interface 208 to present information, such as in the form of dashboards or other user interfaces, regarding the operations of manufacturing facility 170 to computing devices communicably coupled to computing system 200. Such dashboards and user interfaces may present information regarding energy usage and energy savings of manufacturing facility 170. Portal interface 208 may present information that shows overall energy savings of manufacturing facility 170 as well as AHU-level energy savings for manufacturing facility 170.

Portal interface 208 may also present monitoring dashboards that offer near real-time visibility into the performance, health, and operational metrics for manufacturing facility 170. Such monitoring dashboards may present information regarding current operating conditions and/or operational parameters of areas 174 (e.g., clean rooms) of manufacturing facility 170 and associated AHUs 172 in those areas 174 of manufacturing facility 170. Portal interface 208 may present monitoring dashboards that provide detailed visualizations of such information, which may enable users to obtain insights into the operations and energy savings of manufacturing facility 170.

FIG. 3A illustrates an example mapping of HVAC assets to areas of a manufacturing facility, in accordance with one or more aspects of the present disclosure. The HVAC assets may be AHUs (e.g., AHUs 172), and such a mapping may be stored in configuration data store 110 of FIG. 1 and/or configuration data store 210 of FIG. 2.

As illustrated in table 300A, HVAC equipment, such as AHUs 172, that are mapped to clean rooms and/or spaces, which are examples of areas 174 of manufacturing facility 170. For example, air handling unit AHU01 is mapped to area CA024 of manufacturing facility 170 and to area CA076 of manufacturing facility 170. Air handling unit AHU02 is mapped to area CA025 of manufacturing facility 170. Air handling unit AHU03 is also mapped to area CA025 of manufacturing facility 170. As can be seen, the same air handling unit may be mapped to multiple areas 174 of manufacturing facility 170. Further, multiple air handling units may also be mapped to the same area of manufacturing facility 170.

Optimization engine 206 may use such mappings of AHUs 172 to areas 174 of manufacturing facility 170 to determine one or more of AHUs 172 associated with an activity, so that optimization engine 206 may control the operational parameters of the one or more AHUs. As described above, electronic log 144 may, for an activity, specify an area of manufacturing facility 170. Optimization engine 206 may use the mappings of AHUs 172 to areas 174 of manufacturing facility 170 to determine one or more of AHUs 172 associated with the area of manufacturing facility 170 specified for the activity, and may therefore control the operation parameters of the determined one or more AHUs.

FIG. 3B illustrates an example mapping of manufacturing equipment to areas of a manufacturing facility, in accordance with one or more aspects of the present disclosure. Such a mapping may be stored in configuration data store 110 of FIG. 1 and/or configuration data store 210 of FIG. 2. One or more pieces of manufacturing equipment may be mapped to an area of manufacturing facility 170.

As illustrated in table 300B, manufacturing equipment “BQS Blister Machine” having an equipment code of DT-165 may be mapped to clean room CA024 of manufacturing facility 170. Manufacturing equipment “Capsule Filing Machine, SS container” having an equipment code of CD064 may be mapped to clean room CA076 of manufacturing facility 170. Manufacturing equipment “Compression Machine” having an equipment code of CE085 may be mapped to clean room CA075 of manufacturing facility 170.

FIG. 3C illustrates an example mapping of site-specific operational modes to core operational modes, in accordance with one or more aspects of the present disclosure. Such a mapping may be stored in configuration data store 110 of FIG. 1 and/or configuration data store 210 of FIG. 2.

As illustrated in table 300C, given example site-specific operational modes of Line Clearance, Operation, Type A, Type B, PM/Calibration, Cleaning, Predictive Maintenance, and Breakdown, the site-specific operational mode of Line Clearance may be mapped to the at rest mode. The site-specific operational modes of Operation and Type A may each be mapped to the in operation mode. The site-specific operational modes of Type B, PM/Calibration, Cleaning, and Predictive Maintenance may each be mapped to the predictive maintenance and calibration (PMC) mode. The site-specific operational mode of Breakdown may be mapped to the idle mode.

Optimization engine 206 may use such mappings of site-specific operational modes to core operational modes to determine operational modes for AHUs 172 in manufacturing facility 170. As described above, electronic log 144 may, for an activity, specify a site-specific operational mode of one or more AHUs. Optimization engine 206 may use the mappings of site-specific operational modes to core operational modes stored in configuration data store 210 map the specified site-specific operational mode to a core operational mode. Optimization engine 206 may therefore be able to determine operational parameters for the core operational mode and to send instructions to building management system 150 to cause the one or more AHUs to operate according to the operational parameters for the core operational mode.

FIG. 3D illustrates example operational parameters for core operational modes, in accordance with one or more aspects of the present disclosure. Such operational parameters may be in the form of operating condition thresholds for the operational parameters, and may be stored in configuration data store 110 of FIG. 1 and/or configuration data store 210 of FIG. 2.

The operational parameters include operating conditions requirements for airflow, ventilation and filtration (e.g., air changes per hour ACPH) as well as stringent requirements for environmental conditions (e.g., temperature, humidity, pressure gradients, unidirectional airflow, etc.). Such operational parameters help to ensure the quality of the products produced by the facilities, to comply with current good manufacturing practice (cGMP) regulations, and for the safety of personnel working at the facilities.

The operating parameter associated with an operational mode may be designed to achieve one or more particular operating conditions for an area of manufacturing facility 170 given the operational mode. The one or more particular operating conditions may include whether a given AHU in the area is turned on or turned off, the blower speed of the AHU in the area, the temperature in the area, the relative humidity of the area, the differential pressure of the area, and the like.

As illustrated in table 300D, different core operational modes may be associated with different operating conditions. One or more AHUs operating in an area of manufacturing facility 170 may, when operating in a particular core operational mode, operate according to one or more operational parameters to achieve the specified operating conditions associated with the core operational mode. Such mode configurations apply to immediate AHUs and not to parent AHUs.

The operating conditions include AHU Status ON/OFF, AHU Blower Speed %, Room T, Deg C, Room RH %, Room DP Pa, CHW CV % Open, HW CV % Open, and Room ACPH in #. AHU Status ON/OFF specifies whether AHUs are turned on or turned off. AHU Blower Speed % specifies the blower speed percentage of the AHUs. Room T, Deg C specifies the room temperature in Celsius, Room RH % specifies the percentage relative humidity of the room. Room DP Pa specifies the room's differential pressure in Pascals. CHW CV % Open specifies the chilled water control valve percentage open for the room. HW CV % Open specifies hot water control valve percentage open for the room. ACPH in #specifies the air changes per hour.

In some examples, building management system 150 may determine the values of the operating conditions for the different core operational modes. Such values determined by building management system 150 are shown in table 300D as “As per BMS”. In some examples, a user or system may perform quality functional tests to determine the values of the operating conditions for the different core operational modes, and may record the values in a quality functional datasheet (QFDS). Such values recorded in a quality functional datasheet are shown in table 300D as “As per BMS QFDS”, and may be stored in data system 140 shown in FIG. 1.

FIG. 3E illustrates example hierarchies of parent AHUs and child AHUs, in accordance with one or more aspects of the present disclosure. Such hierarchies of parent AHUs and child AHUs may be stored in configuration data store 110 of FIG. 1 and/or configuration data store 210 of FIG. 2.

As illustrated in table 300E, a parent AHU of AHU-01 may have child AHUs of AHU-08, AHU-11, AHU-12, and AHU-14. A parent AHU of AHU-02 may have child AHUs of AHU-03, AHU-04, AHU-05, AHU-06, AHU-07, AHU-08, AHU-09, AHU-10, and AHU-13. A parent AHU of AHU-15 may have child AHUs of AHU-16, AHU-17, and AHU-18.

In some examples, manufacturing facility 170 may have a pressure zoning layout where AHU-01 in the production corridor of manufacturing facility 170, AHU-02 in the production main area corridor of manufacturing facility 170, and AHU-15 in the packing area corridor of manufacturing facility 170 are each maintained at a positive pressure of +25 Pascals. When starting the AHUs, all corridor AHUs (e.g., AHU-01, AHU-02, and AHU-15) are activated first, followed by AHUs in the core cubicles of manufacturing facility 170. That is, the parent AHUs in the corridors are activated first, followed by the child AHUs of those parent AHUs. When shutting down AHUs, the child AHUs in the core cubicles are turned off first, followed by the parent AHUs in the corridors of manufacturing facility 170. Such sequences of starting and shutting parent AHUs and child AHUs may help prevent cross-contamination of air.

FIG. 4 illustrates an example of an electronic log, in accordance with one or more aspects of the present disclosure. As shown in FIG. 4, electronic log 400 is an example of electronic log 144 of FIG. 1.

Electronic log 400 stores a schedule of activities in manufacturing facility 170. Each activity in the schedule of events may be associated with an area (e.g., a room, a space, a zone, etc.) in manufacturing facility 170, out of a plurality of areas 174 in manufacturing facility 170, and/or associated with particular pieces of manufacturing equipment in manufacturing facility 170. Each activity in the schedule of activities may also have an associated start time and end time. Users may interact with data system 140 to modify the schedule of activities in electronic log 144, such as by adding new activities to the schedule of events, rescheduling activities, and/or removing activities.

As shown in FIG. 4, electronic log 400 includes entries 410A-410G (collective “entries 410”). Each entry in electronic log 400 may specify start date 402, room number 404, activity 406, and stop date 408, as well as any other suitable information related to the activity not shown in FIG. 4. Activity 406 of an entry may specify a site-specific operational mode. For example, activity 406 of entry 410A specifies a site-specific operational mode of Type-B. Room number 404 of an entry may specify the area (e.g., room) of manufacturing facility 170 where the activity is taking place. For example, room number 404 of entry 410A specifies that the activity is taking place in rooms CA012 and CA050 of manufacturing facility 170. Start date 402 and stop date 408 may specify the start and end dates and times for the activity, respectively.

Optimization engine 206 may generate a plurality of events from the schedule of activities in electronic log 400. For example, optimization engine 206 may, for each entry of entries 410, generate a pair of events: a start event associated with the start of the activity specified in the entry, and a stop event associated with the end of the activity specified in the entry. Optimization engine 206 may also generate events associated with gaps in the schedule of activities. For example, optimization engine 206 may, for each gap in entries 410 longer than a specified amount of time (e.g., 30 minutes), generate an event associated with an idle activity (e.g., an Idle operational mode).

FIGS. 5A-5D illustrate example user interfaces presented by example energy optimization system 102, in accordance with one or more aspects of the present disclosure. Such user interfaces may be presented by portal interface 108 of FIG. 1 and/or portal interface 208 of FIG. 2 to computing devices (e.g., computing devices 130) communicably coupled to energy optimization system 102 and/or computing system 200.

As shown in FIG. 5A, portal interface 208 may present interface 500A that includes the overall energy savings at a manufacturing facility due to the mode-based energy optimization techniques described in this disclosure. The overall energy savings can be calculated the projected baseline energy consumption minus the actual energy consumption.

The projected baseline energy consumption can be calculated as the average power consumption in operation times the total run hours. The actual energy consumption can be calculated as the sum of the power consumed in each operating mode, where the power consumed in an operating mode is the average power consumption in the operating mode times the total run hours in that operating mode. As can be seen in FIG. 5A, interface 500A shows that the 213,239 kilowatt hours (KWh) of energy consumed over the example period of time, compared with the baseline energy consumption of 218,396 KWh produces energy savings of 5,157 KWh for the manufacturing facility over the example period of time.

As shown in FIG. 5B, portal interface 208 may present interface 500B, which may show energy savings at the AHU-level for a manufacturing facility as well as overall energy savings of the manufacturing facility. For example, interface 500B shows that AHU-22 has a baseline energy consumption of 528 KWh and actual energy consumption of 462 KWh to produce energy savings of 66 KWh. Interface 500B also presents, for AHU-22, the average operating speed of the AHU's variable frequency drive (VFD), with a baseline operating speed of 45 Hertz and an actual operating speed of 43 Hertz. Interface 500B also presents the average power consumption of AHU-22 in each of the four core operational modes: In Operation, At Rest, Maintenance, and Idle, as well as time spent in each of the operational modes.

As shown in FIG. 5C, portal interface 208 may present interface 500C, which is a monitoring dashboard that may offer near real-time visibility into the performance, health, and operational metrics for a manufacturing facility. Interface 500C may be a clean room dashboard that presents, for clean rooms in manufacturing facility 170 the current operating conditions of the clean rooms, including operational parameters and AHUs serving the space. In the example of FIG. 5C, interface 500C shows clean rooms, the AHU(s) serving each clean room, as well as real-time operating conditions of each clean rooms, such as the temperature, relative humidity, and differential pressure of each of the clean rooms.

As shown in FIG. 5D, portal interface 208 may present interface 500D, which is a monitoring dashboard for a specific clean room in manufacturing facility 170. A user may interact with interface 500D to select a specific area (e.g., a clean room) of manufacturing facility 170, and interface 500D may present a visualization of various trends associated with the area of manufacturing facility 170. Interface 500D may present a list of AHUs serving the selected area of manufacturing facility 170, a trend view of AHU parameters for the selected area, a trend view of operating parameters of the area, a trend view that compares the requested operational mode with the operational mode applied to the area, and the like.

The detailed visualizations presented by portal interface 208 may assist users (e.g., oversight management staff of manufacturing facility 170) to obtain key insights into the operations and energy savings of manufacturing facility 170. Such insights may include whether AHUs 172 meet expected operating conditions in the operational modes within the anticipated recovery time, whether AHUs 172 achieve the expected conditions in the rest mode within the designated cleanup time, current operating parameters of areas 174 of manufacturing facility 170, mode transitions over a specified period, and the like. Portal interface 208 may also perform additional processes, such as sending notifications to users (e.g., oversight management staff) indicative of deviations of operating conditions from expected conditions or generating reports on the performance and operational metrics of AHUs 172.

FIG. 6 is a flowchart illustrating example operations performed by an example energy optimization system, in accordance with one or more aspects of the present disclosure. FIG. 6 is described below in the context of FIGS. 1 and 2.

As shown in FIG. 6, one or more processors 240 of computing system 200 may determine an upcoming event associated with an activity associated with an area of a manufacturing facility 170 out of a plurality of areas 174 of a manufacturing facility 170 (602).

In some examples, to determine the upcoming event, the one or more processors 240 may generate a plurality of events from a schedule of activities in the manufacturing facility 170 specified in an electronic log 144, where the electronic log 144 specifies, for each activity in the schedule of activities, a corresponding area in the manufacturing facility 170 out of the plurality of areas 174 in the manufacturing facility 170. The one or more processors 240 may select the upcoming event out of the plurality of events.

In some examples, to select the upcoming event from the electronic log 144, the one or more processors 240 may filter the plurality of events for one or more events associated with the area in the manufacturing facility 170 and may select the upcoming event out of the one or more events associated with the area of the manufacturing facility 170.

One or more processors 240 may identify one or more air handling units (AHUs) associated with the area of the manufacturing facility 170 out of a plurality of AHUs 172 associated with the manufacturing facility 170 (604).

One or more processors 240 may determine, based on the activity, an operational mode for the one or more AHUs out of a plurality of operational modes (606). In some examples, the plurality of operational modes include an in operation mode, a predictive maintenance and calibration mode, an at rest mode, and an idle mode.

One or more processors 240 may send, to a building management system 150, instructions that cause the one or more AHUs to operate according to the operational mode (608).

In some examples, the one or more AHUs include one or more parent AHUs and one or more child AHUs. One or more processors 240 may determine a sequencing of the one or more child AHUs and the one or more parent AHUs to operate according to the operational mode, where the instructions that cause the one or more AHUs to operate according to the operational mode include instructions that cause the one or more child AHUs and the one or more parent AHUs to sequentially operate according to the operational mode based on the sequencing of the one or more child AHUs and the one or more parent AHUs.

In some examples, the sequencing of the one or more child AHUs and the one or more parent AHUs to operate according to the operational mode comprises starting the one or more parent AHUs prior to starting the one or more child AHUs. In some examples, the sequencing of the one or more child AHUs and the one or more parent AHUs to operate according to the operational mode comprises shutting down the one or more child AHUs prior to shutting down the one or more parent AHUs.

Aspects of this disclosure include the following examples.

Clause 1. A computer-implemented method comprising: determining, by a computing system, an upcoming event associated with an activity associated with an area of a manufacturing facility out of a plurality of areas of a manufacturing facility; identifying, by the computing system, one or more air handling units (AHUs) associated with the area of the manufacturing facility out of a plurality of AHUs associated with the manufacturing facility; determining, by the computing system and based on the activity, an operational mode for the one or more AHUs out of a plurality of operational modes; and sending, by the computing system and to a building management system, instructions that cause the one or more AHUs to operate according to the operational mode.

Clause 2. The computer-implemented method of clause 1, further comprising: determining, by the computing system, operational parameters for the one or more AHUs based on the operational mode; wherein the instructions that cause the one or more AHUs to operate according to the operational mode include instructions that cause the one or more AHUs to operate according to the operational parameters.

Clause 3. The computer-implemented method of any of clauses 1 and 2, wherein the one or more AHUs include one or more parent AHUs and one or more child AHUs, further comprising: determining, by the computing system, a sequencing of the one or more child AHUs and the one or more parent AHUs to operate according to the operational mode, wherein the instructions that cause the one or more AHUs to operate according to the operational mode include instructions that cause the one or more child AHUs and the one or more parent AHUs to sequentially operate according to the operational mode based on the sequencing of the one or more child AHUs and the one or more parent AHUs.

Clause 4. The computer-implemented method of clause 3, wherein the sequencing of the one or more child AHUs and the one or more parent AHUs to operate according to the operational mode comprises starting the one or more parent AHUs prior to starting the one or more child AHUs.

Clause 5. The computer-implemented method of any of clauses 3 and 4, wherein the sequencing of the one or more child AHUs and the one or more parent AHUs to operate according to the operational mode comprises shutting down the one or more child AHUs prior to shutting down the one or more parent AHUs.

Clause 6. The computer-implemented method of any of clauses 1-5, wherein the plurality of operational modes include an in operation mode, a predictive maintenance and calibration mode, an at rest mode, and an idle mode.

Clause 7. The computer-implemented method of any of clauses 1-6, wherein determining the upcoming event further comprises: generating, by the computing system, a plurality of events from a schedule of activities in the manufacturing facility specified in an electronic log, wherein the electronic log specifies, for each activity in the schedule of activities, a corresponding area in the manufacturing facility out of the plurality of areas in the manufacturing facility; and selecting, by the computing system, the upcoming event out of the plurality of events.

Clause 8. The computer-implemented method of clause 7, wherein selecting the upcoming event from the electronic log further comprises: filtering, by the computing system, the plurality of events for one or more events associated with the area in the manufacturing facility; and selecting, by the computing system, the upcoming event out of the one or more events associated with the area of the manufacturing facility.

Clause 9. A computing system comprising: memory; and one or more processors communicably coupled to the memory and configured to: determine an upcoming event associated with an activity associated with an area of a manufacturing facility out of a plurality of areas of a manufacturing facility; identify one or more air handling units (AHUs) associated with the area of the manufacturing facility out of a plurality of AHUs associated with the manufacturing facility; determine, based on the activity, an operational mode for the one or more AHUs out of a plurality of operational modes; and send, to a building management system, instructions that cause the one or more AHUs to operate according to the operational mode.

Clause 10. The computing system of clause 9, wherein the one or more processors are further configured to: determine operational parameters for the one or more AHUs based on the operational mode, wherein the instructions that cause the one or more AHUs to operate according to the operational mode include instructions that cause the one or more AHUs to operate according to the operational parameters.

Clause 11. The computing system of any of clauses 9 and 10, wherein the one or more AHUs include one or more parent AHUs and one or more child AHUs, and wherein the one or more processors are further configured to: determine a sequencing of the one or more child AHUs and the one or more parent AHUs to operate according to the operational mode, wherein the instructions that cause the one or more AHUs to operate according to the operational mode include instructions that cause the one or more child AHUs and the one or more parent AHUs to sequentially operate according to the operational mode based on the sequencing of the one or more child AHUs and the one or more parent AHUs.

Clause 12. The computing system of clause 11, wherein the sequencing of the one or more child AHUs and the one or more parent AHUs to operate according to the operational mode comprises starting the one or more parent AHUs prior to starting the one or more child AHUs.

Clause 13. The computing system of any of clauses 11 and 12, wherein the sequencing of the one or more child AHUs and the one or more parent AHUs to operate according to the operational mode comprises shutting down the one or more child AHUs prior to shutting down the one or more parent AHUs.

Clause 14. The computing system of any of clauses 9-13, wherein the plurality of operational modes include an in operation mode, a predictive maintenance and calibration mode, an at rest mode, and an idle mode.

Clause 15. The computing system of any of clauses 9-14, wherein to determine the upcoming event, the one or more processors are further configured to: generate a plurality of events from a schedule of activities in the manufacturing facility specified in an electronic log, wherein the electronic log specifies, for each activity in the schedule of activities, a corresponding area in the manufacturing facility out of the plurality of areas in the manufacturing facility; and select the upcoming event out of the plurality of events.

Clause 16. The computing system of clause 15, wherein to select the upcoming event from the electronic log, the one or more processors are further configured to: filter the plurality of events for one or more events associated with the area in the manufacturing facility; and select the upcoming event out of the one or more events associated with the area of the manufacturing facility.

Clause 17. A non-transitory computer-readable storage medium comprising instructions, that when executed by one or more processors of a computing system, cause the one or more processors to: determine an upcoming event associated with an activity associated with an area of a manufacturing facility out of a plurality of areas of a manufacturing facility; identify one or more air handling units (AHUs) associated with the area of the manufacturing facility out of a plurality of AHUs associated with the manufacturing facility; determine, based on the activity, an operational mode for the one or more AHUs out of a plurality of operational modes; and send, to a building management system, instructions that cause the one or more AHUs to operate according to the operational mode.

Clause 18. The non-transitory computer-readable storage medium of clause 17, wherein the instructions further cause the one or more processors to: determine operational parameters for the one or more AHUs based on the operational mode, wherein the instructions that cause the one or more AHUs to operate according to the operational mode include instructions that cause the one or more AHUs to operate according to the operational parameters.

Clause 19. The non-transitory computer-readable storage medium of any of clauses 17 and 18, wherein the one or more AHUs include one or more parent AHUs and one or more child AHUs, and wherein the instructions further cause the one or more processors to: determine a sequencing of the one or more child AHUs and the one or more parent AHUs to operate according to the operational mode, wherein the instructions that cause the one or more AHUs to operate according to the operational mode include instructions that cause the one or more child AHUs and the one or more parent AHUs to sequentially operate according to the operational mode based on the sequencing of the one or more child AHUs and the one or more parent AHUs.

Clause 20. The non-transitory computer-readable storage medium of clause 19, wherein the sequencing of the one or more child AHUs and the one or more parent AHUs to operate according to the operational mode comprises starting the one or more parent AHUs prior to starting the one or more child AHUs.

In one or more examples, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored on or transmitted over, as one or more instructions or code, a computer-readable medium and executed by a hardware-based processing unit. Computer-readable media may include computer-readable storage media, which corresponds to a tangible medium such as data storage media, or communication media including any medium that facilitates transfer of a computer program from one place to another, e.g., according to a communication protocol. In this manner, computer-readable media generally may correspond to (1) tangible computer-readable storage media, which is non-transitory or (2) a communication medium such as a signal or carrier wave. Data storage media may be any available media that may be accessed by one or more computers or one or more processors to retrieve instructions, code and/or data structures for implementation of the techniques described in this disclosure. A computer program product may include a computer-readable medium.

By way of example, and not limitation, such computer-readable storage media may comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage, or other magnetic storage devices, flash memory, or any other storage medium that may be used to store desired program code in the form of instructions or data structures and that may be accessed by a computer. Also, any connection is properly termed a computer-readable medium. For example, if instructions are transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. It should be understood, however, that computer-readable storage media and data storage media do not include connections, carrier waves, signals, or other transient media, but are instead directed to non-transient, tangible storage media. Disk and disc, as used herein, includes compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray disc, where disks usually reproduce data magnetically, while disks reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.

Instructions may be executed by one or more processors, such as one or more digital signal processors (DSPs), general purpose microprocessors, application specific integrated circuits (ASICs), field programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuitry. Accordingly, the term “processor,” as used herein may refer to any of the foregoing structures or any other structure suitable for implementation of the techniques described herein. In addition, in some aspects, the functionality described herein may be provided within dedicated hardware and/or software modules. Also, the techniques could be fully implemented in one or more circuits or logic elements.

The techniques of this disclosure may be implemented in a wide variety of devices or apparatuses, including a wireless handset, an integrated circuit (IC) or a set of ICs (e.g., a chip set). Various components, modules, or units are described in this disclosure to emphasize functional aspects of devices configured to perform the disclosed techniques, but do not necessarily require realization by different hardware units. Rather, as described above, various units may be combined in a hardware unit or provided by a collection of intraoperative hardware units, including one or more processors as described above, in conjunction with suitable software and/or firmware.

It is to be recognized that, depending on the example, certain acts or events of any of the methods described herein may be performed in a different sequence, may be added, merged, or left out altogether (e.g., not all described acts or events are necessary for the practice of the method). Moreover, in certain embodiments, acts or events may be performed concurrently, e.g., through multi-threaded processing, interrupt processing, or multiple processors, rather than sequentially.

In some examples, a computer-readable storage medium comprises a non-transitory medium. The term “non-transitory” indicates that the storage medium is not embodied in a carrier wave or a propagated signal. In certain examples, a non-transitory storage medium may store data that can, over time, change (e.g., in RAM or cache).

Various examples have been described. These and other examples are within the scope of the following claims.