Set point optimization in multi-resolution processes

Methods and systems for determining an optimized set point for a manufacturing apparatus are described. In an example, a processor may receive observed data from the manufacturing apparatus. The observed data may include data collected by the manufacturing apparatus based on at least one resolution. The processor may generate feature data based on the observed data. The processor may determine a first model and a second model based on the feature data. The first model may relate to a first prediction of a key performance indicator of the manufacturing apparatus in a first amount of future time. The second model may relate to a second prediction of the key performance indicator of the manufacturing apparatus in a second amount of future time. The processor may determine the optimized set point based on an objective relating to the first model and based on a constraint relating to the second model.

FIELD

The present application relates generally to computers, and computer applications, and more particularly to computer-implemented methods and systems in machine learning and manufacturing processing systems.

BACKGROUND

In manufacturing physical processes (e.g., aluminum smelting process, oil production process, wind turbine energy generation process, aerospace equipment production process, and/or other processes), it is very important to keep variables operating at optimal set points to maximize efficiency and reduce possible excursions, such as setting the optimal speed for automated cars, optimal resistance for aluminum smelting processes, and other optimizations in other manufacturing processes. Conventional solutions are often based on the experience of the operators and/or engineers to manually assign the set points. In relatively simple processes, the solutions may be based on conventional physics and engineering models.

SUMMARY

In some examples, a method for determining an optimized set point for a manufacturing apparatus is generally described. The method comprising receiving, by a processor, observed data from the manufacturing apparatus. The observed data may include data collected by the manufacturing apparatus based on at least one resolution. The method may further comprise generating, by the processor, feature data based on the observed data. The method may further comprise determining, by the processor, a first model based on the feature data. The first model may relate to a first prediction of a key performance indicator of the manufacturing apparatus in a first amount of future time. The method may further comprise determining, by the processor, a second model based on the feature data. The second model may relate to a second prediction of the key performance indicator of the manufacturing apparatus in a second amount of future time. The method may further comprise determining, by the processor, the optimized set point based on an objective relating to the first model and based on a constraint relating to the second model.

In some examples, a system effective to determine an optimized set point for a manufacturing apparatus is generally described. The system comprising a memory configured to store a set of instructions indicating an objective and a constraint. The system may further comprise a processor configured to be in communication with the memory. The system may further comprise a machine learning module configured to be in communication with the processor. The system may further comprise an optimization module configured to be in communication with the processor and the machine learning module. The processor may be configured to receive observed data from the manufacturing apparatus. The observed data may include data collected by the manufacturing apparatus based on at least one resolution. The processor may be further configured to generate feature data based on the observed data. The processor may be further configured to send the feature data to the machine learning module. The machine learning module may be configured to determine a first model based on the feature data. The first model may relate to a first prediction of a key performance indicator of the manufacturing apparatus in a first amount of future time. The machine learning module may be further configured to determine a second model based on the feature data. The second model may relate to a second prediction of the key performance indicator of the manufacturing apparatus in a second amount of future time. The machine learning module may be further configured to send the first model and the second model to the optimization module. The optimization module may be configured to determine the optimized set point based on the objective and the constraint. The objective may relate to the first model and the constraint may relate to the second model.

In some examples, a computer program product for determining an optimized set point for a manufacturing apparatus is generally described. The computer program product may include a computer readable storage medium having program instructions embodied therewith. The program instructions may be executable by a processing element of a device to cause the device to perform one or more methods described herein.

DETAILED DESCRIPTION

In some examples, physical processes used in manufacturing have phenomena with multiple resolutions, such as temporal resolutions. Physics-based models tend to be very complex and cannot be parameterized with sensor data in near real time for operational improvement. Data-driven machine learning models provide feasible computational alternative to assimilate sensor data into predictive and prescriptive models. But the challenges are to handle multiple different resolutions (e.g., temporal resolutions) that are relevant for the desired key performance indicators (KPI), such as yield and efficiency. In order to address the challenge of handling multiple different resolutions, it is necessary to exploit multi-resolution sensor data collected from the manufacturing processes in order to reveal important information, such that optimal control set points may be established. Therefore, a system is needed to fuse information collected at different resolutions in order to provide better prediction in manufacturing systems.

For example, in an aluminum smelting processing environment involving electrolyzed chemical interactions in a pot room, multiple different resolutions of data may be collected by various sensors. Some example data collection may include capturing extremely fast changes in the pot room, such as noise, every ten seconds; capturing data from relatively faster processes every five minutes; or capturing relatively slow processes, such as temperature change, on a daily basis. Since different types of data are being collected at different time intervals (temporal resolutions), there is a need to fuse the collected data to accurately assess a performance of the processing system.

A system in accordance with the present disclosure (e.g., system100shown inFIG. 1) may facilitate building a predictive machine learning model using observed variables (e.g., data collected from a manufacturing process or system) to exploit important information from multi-resolution sensor data, learn the behaviors of manufacturing physical processes at different set points, establish the optimal set points to improve near future yield and efficiency while keeping the processes stable in the far future, and provide a path for moving poorly performing processes to new set points that improve performance.

To be further described below, the system100may provide advisory control set points to improve yield and efficiency for multi-resolution processes. For example, the system100may build machine learning predictive model(s) for multi-resolution process data by creating desired features from finer resolution, learning the model(s) based on the created features, and building an optimization framework for seeking the best control set points. The system100may also dynamically change the set points based on the condition of the processes at certain time periods, and continuously adjust the set points when new data is available.

Also described in more detail below, the system100may use available data, or observed data, to build machine learning predictive model(s) in order to predict a near efficiency function and a far future efficiency function, where the predicted functions are functions of observed variables and the set point variables. In an example, if the observed variables are independent from the set point variables, the system100may search for a best value of the set point variables to maximize the near efficiency while the far future efficiency does not significantly degrade. The search may be obtained by solving an optimization problem relating to a maximization of the near efficiency function subject to a constraint relating to the far future efficiency function, where the constraint is based on a parameter to control a degradability of the far future efficiency function. In an example, the solution to the optimization problem may be based on a search over all possible choices of the set point variables when a space of the set point variables is finite and small. The system100may, based on the solution to the optimization problem, establish a new set point that may attain the best predicted efficiency outcomes of the near efficiency function and the far future efficiency function.

In another example, a challenge that may be addressed by the system100may be dependencies of the observed variables on the set point variables, since changes in the set point variables may lead to changes in the dependent observed variables as well. In examples where the observed variables include both dependent and independent observed variables, the system100may build regression models relating to the dependent observed variables. The system100may then perform the search for a best value of the set point variables in order to optimize the near efficiency function based on both the dependent and independent observed variables, subject to a constraint relating to the far future efficiency function.

FIG. 1illustrates an example computer system100that can be utilized to implement set point optimization in multi-resolution processes, arranged in accordance with at least some embodiments described herein. In some examples, the system100may be implemented by a device101to facilitate a determination of an optimized set point142to be inputted into a manufacturing apparatus102, where the manufacturing apparatus102may be operable to output a product103, and may include a computer system configured to monitor operations of the manufacturing apparatus102. For example, manufacturing apparatus102may produce oil, energy, aerospace equipments, processed aluminum, and/or other products. In some examples, the device101may be a computing device such as a desktop computer, a laptop computer, a server, and/or a device that may include a plurality of processing elements. The device101may be configured to be in communication with the manufacturing apparatus102directly, such as, via a wire and/or cable, or wirelessly (e.g., BLUETOOTH, near-field communication, Internet, WI-FI, cellular network, and/or other wireless communication protocol). In some examples, device101may be a part of manufacturing apparatus102, such as a computing device of the manufacturing apparatus102.

In an example embodiment, the device101may include a processor120, a memory122, a machine learning module130, and/or an optimization module140, that may be configured to be in communication with each other. The processor120may be a central processing unit of the device101. In some examples, the processor120may be configured to control operations of the machine learning module130and the optimization module140. In some examples, the machine learning module130and the optimization module140may be hardware components such as programmable logic devices, microcontrollers, memory devices, and/or other hardware components, of the processor120and/or the device110. In some examples, the machine learning module130and the optimization module140may be software modules that may be implemented with the processor120to perform one or more tasks. In some examples, the machine learning module130and the optimization module140may be packaged as an application that may be controlled and/or executed by the processor120of the device101in order to implement the system100.

The memory122may be configured to selectively store instructions executable by the processor120, the machine learning module130, and the optimization module140. For example, in one embodiment, the memory122may store a set of optimization instructions124, where the optimization instructions124may include instructions, such as executable code, related to machine learning algorithms. The optimization instructions124may also indicate objectives and constraints relating to at least one key performance indicator of the manufacturing apparatus102. The processor120, the machine learning module130, and the optimization module140may each be configured to execute one or more portions of the optimization instructions124in order to facilitate implementation of the system100.

In an example embodiment, the manufacturing apparatus102may provide observed data105to the device101, in order for device101to determine the optimized set point142for the manufacturing apparatus102. The observed data105may include input set points106, sensor data107, key performance indicator (KPI) data108, and/or other data related to production of product103. The input set points106may include set points that have been inputted into the manufacturing apparatus102at one or more past time instances, or that may have been used by the manufacturing apparatus. Examples of a set point may include an amount of raw materials inputted into manufacturing apparatus, a temperature relating to the production process of the product103that may have been set by an operator of the manufacturing apparatus102, a speed to operate mechanisms of manufacturing apparatus102, and/or other parameters that may be set, controlled, adjusted, or changed, by an operator of the manufacturing apparatus102. The sensor data107may include data collected from one or more homogeneous and/or heterogeneous sensors, such as data relating to temperature, pressure, humidity, and/or other sensor data relating to the production of the product103. The sensor data107may include multi-resolution data, such as data collected from the sensors of manufacturing apparatus102at different time intervals. The key performance indicator data108may include data relating to one or more key performance indicators relating to production of the product103performed by the manufacturing apparatus102, such as efficiency, throughput, energy consumption, yield, manufacturing cycle time, and/or other key performance indicators.

The device101, or the processor120, may receive the observed data105and may store the received observed data105in memory122. Processor120may be configured to generate, or derive, feature data109based on the sensor data107, such as by transforming the sensor data107into a format that may be used by machine learning module130, extracting noise data from the sensor data107, combining multi-resolution data among the sensor data107, identifying a particular statistic (e.g., average, median, and/or other statistics) of the sensor data107, and/or performing other transformations (further described below). The processor120may send the feature data109, the input set points106, and the key performance indicator data108to the machine learning module130.

The machine learning module130may learn, or determine, one or more models132based on the feature data109, the input set points106, and the key performance indicator data108. In some examples, the sensor data107may be in a format suitable for machine learning module, or may include data of an appropriate resolution, such that learning of the models132may be further based on the sensor data107. The machine learning module130may apply machine learning algorithms among optimization instructions124on the feature data109, the input set points106, and the key performance indicator data108to learn one or more models132. As will be further described below, the machine learning module130may learn a first model (denoted as f1) and a second model (denoted as f2), where the first model f1may relate to a near future KPI and the second model f2may relate to a far future KPI (further described below). The machine learning module130may send the learned models132to the optimization module140. In some examples, and further described below, the machine learning module130may be configured to learn regression models representing the observed variables (e.g., the sensor data107) that may be dependent on the observed set points (e.g., input set points106).

The optimization module140may apply the optimization instructions124on the models132, which includes models f1and f2, to determine the optimized set point142. The optimization instructions124may indicate one or more conditions and constraints for the optimization module140to optimize models132and to determine the optimized set point142. For example, the optimization instructions124may indicate that in order to optimize a model relating to throughput, the optimization module140may need to optimize the model by maximizing an output of the model subject to a constraint relating to throughput. In another example, the optimization instructions124may indicate that in order to optimize a model relating to energy consumption, the optimization module140may need to optimize the model by minimizing an output of the model subject to a constraint relating to energy consumption. By optimizing models132according to the optimization instructions124, optimization module140may determine the optimized set points142that may optimize a manufacturing process to produce product103.

Device101may provide the optimized set point142to the manufacturing apparatus102, and may set, or define, the optimized set point142as an input to the manufacturing apparatus102, such that subsequent instances of production of product103may be based on the optimized set point142. Manufacturing apparatus102may continue to provide updated observed data105, including one or more of updated values of the input set points106, the sensor data107, and the KPI data108, in order for device101to update the optimized set point142for the manufacturing apparatus102.

FIG. 2illustrates the example system ofFIG. 1with additional details relating to set point optimization in multi-resolution processes, arranged in accordance with at least some embodiments described herein.FIG. 2is substantially similar to computer system100ofFIG. 1, with additional details. Those components inFIG. 2that are labeled identically to components ofFIG. 1function as described with reference toFIG. 1.

As mentioned above, the processor120may be configured to generate the feature data109based on the sensor data107collected from different sensors and at different time intervals. In an example shown inFIG. 2, a first sensor may collect sensor data including one or more data points203at a first time interval represented as a resolution202, and a second sensor may collect sensor data including one or more data points205at a second time interval represented as a resolution204. The first sensor and the second sensor may be homogeneous or heterogeneous sensors. The processor120may analyze the data points203collected at the resolution202and the data points205collected at the resolution204according to optimization instructions124. Although two resolutions are shown in the example presented inFIG. 2, in some examples, data collected at more than two resolutions may also be analyzed by system100.

In an example, the optimization instructions124may indicate an embodiment to extract data points from a higher resolution among the resolution202and the resolution204. The processor120may search for, or identify, one or more time instances where data were collected by the first and second sensors at the resolution202and the resolution204, respectively, and extract the data point from the higher resolution (e.g., resolution204) at the identified time instances. For example, the processor120may determine that the resolution202is the lower resolution and, in response, may extract data points205collected at resolution204at the time intervals where data are also collected at the resolution202. In the example shown inFIG. 2, the processor120may not extract any data points at time210due to the fact that no data point was collected at resolution202by the first sensor. At time211, the processor120may extract data point205since data were collected at both the resolution202and the resolution204at time211. Similarly, the processor120may extract data point205since data were collected at both the resolution202and the resolution204at time212. The processor120may continue to extract data points205from resolution204and upon a completion of the extraction, the processor120may compile the extracted data points205to derive, or generate, feature data109.

In another example, the optimization instructions124may indicate another embodiment to derive the feature data109by determining an average between data points from both the resolution202and the resolution204. The processor120may search for, or identify, one or more time instances where data were collected by the first and second sensors at the resolution202and the resolution204, respectively, and determine an average value between the data points from the resolution202and the resolution204at the identified time instances.

The optimization instructions124may indicate further embodiments to derive feature data109, such as identifying a median among data points from different resolutions, applying window shifting techniques on data points from particular resolutions, and/or other analysis techniques, depending on a desired implementation of system100.

The processor120may derive more than one set of feature data109. For example, the processor120may derive a set of feature data representative of temperature, a set of feature data representative of pressure, and/or feature data for other attributes relating to production of product103.

FIG. 3illustrates the example system ofFIG. 1with additional details relating to set point optimization in multi-resolution processes, arranged in accordance with at least some embodiments described herein.FIG. 3is substantially similar to computer system100ofFIG. 1, with additional details. Those components inFIG. 3that are labeled identically to components ofFIG. 1function as described with reference toFIG. 1.

The machine learning module130may receive the feature data109(denoted as Xi), the input set points106(denoted as Zi), and the KPI data108(denoted as Yi) from the processor120. In an example, each Yimay be an output of a function associated with a process P used by the manufacturing apparatus102to produce product103, where the function may be dependent on corresponding Xiand Zi(e.g., Yi=P(Xi, Zi)). In examples where the sensor data107do not require transformation into the feature data109, Ximay also denote the sensor data107. The machine learning module130may apply machine learning algorithms on the received data to learn models132. In an example, the machine learning module130may identify the input set points106as training inputs, the KPI data108as training labels, and the feature data109as weights or dimensions of the input set points, depending on a dependency of the feature data109on input set points106. For example, when the feature data109is independent from input set points106(e.g., changing a value of Ziwould not change a value of Xi), the feature data109may be weights that may be applied on the input set points106to change a value of the KPI data108. When the feature data109is dependent on input set points106(e.g., changing a value of Ziwould change a value of Xi), the feature data109may be a dimension of the input set points106, such as a variable among a function representative of the input set points106.

The machine learning module130may learn a first model f1, which may be a model representative of a near future KPI prediction denoted as Ynear. A near future KPI prediction include a predicted efficiency of the manufacturing apparatus102, a predicted energy consumption of the manufacturing apparatus102, a predicted throughput of the manufacturing apparatus102, in a near future such as a next hour, a next day, a next two days, and/or other amount of time.

The machine learning module130may learn a second model f2, which may be a model representative of a far future KPI prediction denoted as Yfar. A far future KPI prediction include a predicted efficiency of the manufacturing apparatus102, a predicted energy consumption of the manufacturing apparatus102, a predicted throughput of the manufacturing apparatus102, in a far future such as a next month, a next year, and/or other amount of time.

When the feature data109is independent from the set point variable Z, the near future KPI prediction Ynearand the far future KPI prediction Yfarmay be denoted as:
Ynear=f1(X1,X2, . . . ,Xd,Z)
Yfar=f2(X1,X2, . . . ,Xd,Z)
where X1, X2, . . . , Xdare independent groups of observed variables (e.g., each group corresponds to an attribute, such as a group for temperature, a group for pressure, etc.) that were used by the machine learning module130to learn models132(or formation of Ynearand Yfar), and Z denotes the set point variable that needs to be optimized by the optimization module140. In an example embodiment, an optimization of the variable Z is an identification of an optimal value of Z based on one or more constraints, which will be further described below. In some examples, the models f1and f2may model a manufacturing process that can be executed by the manufacturing apparatus102, where the manufacturing process may be of a high complexity. As such, the system100may provide a solution to address a challenge of analyzing performances of highly complex manufacturing processes by using machine learning techniques to train, or estimate, models that may be used to analyze various KPIs of the manufacturing processes.

When at least a portion of the feature data109is dependent from the set point variable Z, the dependent portion of the feature data109may be denoted as X1, X2, . . . , Xaand the independent portion of the feature data109may be denoted as Xa+1, . . . , Xd. The machine learning module130may learn one or more regression models g1, g2, . . . , ga, based on relationship between the dependent feature data Xi(i=1, . . . , a) with the set point variable Z, and based on independent variables Xa+1, . . . , Xd.

The amount of time that constitutes a near future and a far future may be dependent on a desired implementation of system100, or may be arbitrarily defined by a user of system100, or by an operator of manufacturing apparatus102, etc. The amount of time in the near future time and the far future time may begin at a current time. The near future time and the far future time may include overlapping time instances, or non-overlapping time instances. In an example, an operator of the manufacturing apparatus102who may be looking to solve an efficiency problem in production of product103with the next week may consider one day as a near future amount of time, and one week as a far future amount of time. An operator of the manufacturing apparatus102who may be looking to solve an efficiency problem in production of product103with the next year may consider one month as a near future amount of time, and one year as a far future amount of time.

In some examples, the amount of training data used by machine learning module130to learn models132may be based on the defined amount of time for the near future and the far future. If the near future is defined as one week, the machine learning module130may learn model f1based on approximately one week of data among the feature data109, the input set points106, and the KPI data108. If the far future is defined as one year, the machine learning module130may learn model f2based on approximately one year of data among the feature data109, the input set points106, and the KPI data108.

Upon learning models132, the machine learning module130may send the learned models132to the optimization module140.

The optimization module140may apply objectives and constraints indicated by optimization instruction124on models132, in order to determine the optimized set point142, which may be an optimal value of the set point variable Z.

In an example, system100may be implemented to determine the optimized set point142that may lead to an optimal efficiency of the manufacturing apparatus102. The optimization instruction124may indicate that in order to identify the optimized set point142for optimal efficiency, the model f1needs to be maximized subject to a constraint that the model f2needs to be greater than a parameter302(denoted as c), where parameter302may be a predefined value that controls a degradability of the KPI prediction Yfar.

In examples where the observed variables X (e.g., feature data109) are independent from the set point variable X, the objective and constraint to determine an optimal set point for optimal efficiency may be denoted as:
maxzf1(X1,X2, . . . ,Xd,Z) subject to:f2(X1,X2, . . . ,Xd,Z)>c

The optimization module140may search for a best value of Z that maximizes model f1and yet, constrain the model f2to be greater than parameter c. In an example embodiment, the optimization module140may search all possible choices of Z when a space of Z is finite and small. Upon a completion of the search, the optimization module140may identify a value of the set point variable Z that attains the optimal predicted efficiencies Ynearand Yfar.

In examples where the observed variables X (e.g., feature data109) include dependent and/or independent variables from the set point variable X, the objective and constraint to determine an optimal set point for optimal efficiency may be denoted as:
maxzf1(g1, . . . ,ga,Xa+1, . . . ,Xd,Z) subject to:f2(g1, . . . ,ga,Xa+1, . . . ,Xd,Z)>c

In some examples, a value of the parameter302may be set by an operator of the manufacturing apparatus102. In some examples, the optimal predicted efficiencies Ynearand Yfarmay change over time when the models f1and f2are regression models. The system100may retrain the models f1and f2to over time in order to adapt to the dynamics of the manufacturing process being executed by the manufacturing apparatus102.

In another example, system100may be implemented to determine the optimized set point142that may lead to an optimal energy consumption by the manufacturing apparatus102. The optimization instructions124may indicate that in order to identify the optimized set point142for optimal energy consumption, the model f1needs to be minimized subject to a constraint that the model f2needs to be less than the parameter302. In other examples, the objectives and constraints to determine the optimized set point142may be based on the KPI that needs to be optimized.

FIG. 4illustrates an example process from an implementation of the example system ofFIG. 1relating to set point optimization in multi-resolution processes, arranged in accordance with at least some embodiments described herein. At least some of the components inFIG. 4may be described below with reference to components fromFIG. 1.

An example process400shown inFIG. 4relates to an embodiment where system100continues to receive new data from the manufacturing apparatus102in order to update the optimized set point142, such that an efficiency of the manufacturing apparatus may be maintained at a desirable level. In the example process400, new data, such as new observed data, may be input into system100. The new observed data may include updated values of the observed data105, and/or may include updated values of the input set points106, the sensor data107, and/or the KPI data108. The system100may determine new models based on the newly received data in order to update efficiency predictions Ynearand Yfar.

The system100may compare the updated efficiency prediction Ynearwith a current efficiency of the manufacturing apparatus102. If the updated efficiency prediction Ynearis greater than the current efficiency, then the system100may determine that set point Z does not need to be changed since the predicted efficiency is greater than, or an improvement over, the current efficiency. When no change is needed for set point Z, the system100may continue to wait for a next set of new data from the manufacturing apparatus102.

If the updated efficiency prediction Ynearis less then than the current efficiency, then the system100may determine that set point Z needs to be changed since the predicted efficiency is less than, or fails to improve over, the current efficiency. The system100may execute the objectives and constraints relating to efficiency, which may be indicated by the optimization instruction124stored in memory122, to determine a new optimized set point142(optimal set point Z).

The system100may continue to receive new observed data, and for each set of received observed data, perform comparisons between predicted near future KPI with a current KPI to determine if changes are needed to the set point Z. As such, a performance of the manufacturing apparatus102may be maintained as manufacturing apparatus102is being run to produce product103. Further, the system100may automatically update the updated set points at the manufacturing apparatus102such that an operator of the manufacturing apparatus102may not need to manually define a new set point in response to new sets of observe data at future instances.

FIG. 5illustrates an example process from an implementation of the example system ofFIG. 1relating to set point optimization in multi-resolution processes, arranged in accordance with at least some embodiments described herein. At least some of the components inFIG. 5may be described below with reference to components fromFIG. 1.

As shown in a plot, or graph, of efficiency vs. time inFIG. 5(labeled as efficiency example510), an efficiency512of the manufacturing apparatus102, prior to an implementation of the system100, may be improved to an optimized efficiency514upon the implementation of the system100, where the optimized efficiency514is greater than the efficiency512at one or more time instances.

FIG. 6illustrates an example process from an implementation of the example system ofFIG. 1relating to set point optimization in multi-resolution processes, arranged in accordance with at least some embodiments described herein. At least some of the components inFIG. 6may be described below with reference to components fromFIG. 1.

As shown in a plot, or graph, of energy consumption vs. time inFIG. 6(labeled as energy consumption example620), an energy consumption622of the manufacturing apparatus102, prior to an implementation of the system100, may be improved to an optimized energy consumption624upon the implementation of the system100, where the optimized energy consumption624is less than the efficiency622at one or more time instances.

FIG. 7illustrates a flow diagram relating to set point optimization in multi-resolution processes, arranged in accordance with at least some embodiments presented herein. The process inFIG. 7may be implemented using, for example, computer system100discussed above. An example process may include one or more operations, actions, or functions as illustrated by one or more of blocks702,704,706,708,710,712, and/or714. Although illustrated as discrete blocks, various blocks may be divided into additional blocks, combined into fewer blocks, eliminated, or performed in parallel, depending on the desired implementation.

Processing may begin at block702, where a processor may receive observed data from a manufacturing apparatus. The observed data may include sensor data collected by the manufacturing apparatus based on at least one time resolution.

Processing may continue from block702to block704. At block704, the processor may generate feature data based on the observed data.

Processing may continue from block704to block706. At block706, the processor may determine a first model based on the feature data. The first model may relate to a first prediction of a key performance indicator of the manufacturing apparatus in a first amount of future time (e.g., the near future time described above).

Processing may continue from block706to block708. At block708, the processor may determine a second model based on the feature data. The second model may relate to a second prediction of the key performance indicator of the manufacturing apparatus in a second amount of future time. The second amount of future time may be greater than the first amount of future time (e.g., the far future time described above).

Processing may continue from block708to block710. At block710, the processor may compare the first prediction of the key performance indicator with a current key performance indicator of the manufacturing apparatus. If the predicted KPI is an improvement over the current KPI, the process may return to block702, where the process may wait for a next receipt of new observed data from the manufacturing apparatus. If the predicted KPI is not an improvement over the current KPI, the process may continue to block712.

Processing may continue from block710to block712. At block712, the processor may determine the optimized set point based on an objective relating to the first model and based on a constraint relating to the second model.

Processing may continue from block712to block714. At block714, the processor may send the optimized set point to the manufacturing apparatus.