A chemical system for an operation exhibiting steady-state gain inversion is provided herein and includes a reactor configured to receive a feed stream and produce an outlet stream to form a process and a control device configured to control a process. The control device receives inputs indicative of an operational parameter and output variables and, in response to the inputs and output variables, provides a steady-state manipulated input configured to control or optimize the process. The control device includes an input disturbance model, a state estimator, a non-linear steady-state target calculator, and a regulator configured to provide a signal for adjustment of one or more inputs based on the steady-state manipulated input and associated output variables.

A method of probing a multidimensional parameter space of battery cell test protocols is provided that includes defining a parameter space for a plurality of battery cells under test, discretizing the parameter space, collecting a preliminary set of cells being cycled to failure for sampling policies from across the parameter space and include multiple repetitions of the policy, specifying resource hyperparameters, parameter space hyperparameters, and algorithm hyperparameters, selecting a random subset of charging policies, testing the random subset of charging policies until a number of cycles required for early prediction of battery lifetime is achieved, inputting cycle data for early prediction into an early prediction algorithm to obtain early predictions, inputting the early predictions into an optimal experimental design (OED) algorithm to obtain recommendations for running at least one next test, running the recommended tests by repeating from the random subset testing step above, and validating final recommended policies.

A data prediction method and apparatus generate plant normal state prediction data based on measurement data of multiple tags and a plant prediction model, to enhance accuracy of anomaly/fault prediction by providing precise prediction data in the normal state even in a plant anomaly/fault condition. The method includes generating primary prediction data by performing primary prediction based on the measurement data and the plant prediction model; selecting an anomalous state tag among the multiple tags, the selected anomalous state tag determined as data of an anomalous state based on measurement data corresponding to the primary prediction data; updating the plant prediction model by using the measurement data of only normal state tags; and generating secondary prediction data by performing secondary prediction based on the measurement data of the normal state tags and the updated plant prediction model. Secondary prediction is performed only when an anomalous state tag is selected.

Systems and methods may use a low speed controller in addition to an economic optimizer to achieve long-term objectives without significantly disrupting or destabilizing an electrical system. Specific long-term objectives include maximizing a capacity factor incentive and regulating battery degradation, but the methods and systems herein can be extended to more long-term objectives. A low speed controller can adjust one or more parameters of a cost function based on the relation between the projected state of the electrical system and the one or more parameters to effectuate a change to the electrical system to attempt to comply with the long-term objective.

The disclosure relates to a sensor network, machine type communication (MTC), machine-to-machine (M2M) communication, and technology for internet of things (IoT). A method of a server is provided. The method includes determining a target temperature range to be applied to a first zone; predicting an indoor temperature for each of a plurality of zones included in a second zone in which the first zone is included; predicting efficiency of at least one first outdoor unit connected to first indoor units installed at the second zone; and controlling operations of the first indoor units based on the target temperature range, the indoor temperature for each of the plurality of zones, and the efficiency of at least one first outdoor unit.

Some embodiments provide a distributed optimization framework and technology for the control of aggregated propulsion system components that iteratively operates until a commanded propulsion-related profile is produced by aggregated components of the propulsion system. Some embodiments use a distributed iterative solution in which each component solves a local optimization problem with local constraints and states, while using global variables that are based upon information from each other component. The global variables may be determined via a distributed transaction system using component-specific information obtained from each component at each iteration. The global variables may be broadcast to the components for each new iteration.

A vehicle can include: a solar charging panel mounted to the vehicle and configured to acquire solar energy for charging a battery of the vehicle; a communication module configured to collect weather information and date-and-time information at a plurality of positions along a traveling path from an origin to a destination; a storage configured to store road information; and a controller configured to calculate a solar charging energy of the traveling path by predicting a maximum solar charging energy to be charged through the solar charging panel based on the weather information collected at the plurality of positions along the traveling path, and to calculate a solar charging prediction amount based on the date-and-time information collected at the plurality of positions along the traveling path, an amount by which to reduce the solar charging energy determined according to the stored road information relating to the traveling path, and the predicted maximum solar charging energy.

A field device, method, and non-transitory computer readable medium provide for cost of lost opportunity to operators in real-time using an advance process control. The field device includes a memory and a processor operably connected to the memory. The processor receives current values and average values for controlled variables and manipulated variables; determines costs of lost opportunity for each of controlled variable variance issues, limit issues, model quality issues, inferential quality issues, and variable model issues based on the current values and the average values of the controlled variables; and stores the costs of lost opportunity for the field device.

Systems and methods provide a new paradigm of Advanced Process Control that includes building and deploying APC seed models. Embodiments provide automated data cleansing and selection in model identification and adaption in multivariable process control (MPC) techniques. Rather than plant pre-testing onsite for building APC seed models, the embodiments help APC engineers to build APC seed models from existing plant historical data with self-learning automation and pattern recognition, AI techniques. Embodiments further provide “growing” and “calibrating” the APC seed models online with non-invasive closed loop step testing techniques. PID loops and associated SP, PV, and OPs are searched and identified. Only “informative moves” data is screened, identified, and selected among a long history of process variables for seed model development and MPC application. The seed models are efficiently developed while skipping the costly traditional pre-testing steps and minimizing the interferences to the subject production process.

An apparatus is provided, which includes a setting unit for setting an operation content for a manufacturing system configured to manufacture an object to be manufactured, a first acquisition unit for acquiring a posterior state parameter set indicating a state of at least one of the manufacturing system or the object to be manufactured after the operation content is set, and a learning processing unit for executing, by using learning data including the operation content and the posterior state parameter set, a learning process of a control model of the manufacturing system configured to output the operation content that increases a reward value determined by a preset reward function in response to input of a state parameter set indicating a state of at least one of the manufacturing system or the object to be manufactured.