An intelligent control system and method of thin plate dryer for cut tobacco are provided. The system includes a factor searching and screening unit, a control unit, an early warning unit. The control unit adopts a dual-model control method and establishes a process parameter control model and an energy balance model, the control unit calculates the moisture discharge opening value in real time according to the dual-model; the early warning unit is configured to connected with the control unit, and send out an alarm information based on early warning signal. The present disclosure is designed to transform the traditional control into intelligent precision control, improve product quality, reduce product differences between batches and build an intelligent early warning function.

A system control apparatus includes a proportional-integral-derivative (PID) controller configured to control a behavior of a system and having a gain, and a gain determiner configured to apply, to a set adaptive load model, a variable associated with an error that varies based on a load change of the system and adaptively vary the gain using the adaptive load model to which the variable associated with the error is applied.

A system can include a gas turbine and a processing system. The gas turbine can include a compressor coupled to a turbine through a shaft. The processing system can be configured to: automatically transition an operating condition of the system through a plurality of operating states; determine an efficiency of the system at each of a plurality of the operating states; for each of the plurality of operating states: select a future operating state of the system based on the determined efficiency of the current operating state.

A hierarchical resource management system for a building includes one or more processors. The processors implement a plurality of agents that each monitor sensed values, and generate operating scenarios based on the sensed values for corresponding resources. The processors also implement a coordinator that filters the operating scenarios to remove the operating scenarios that violate internal laws of the agents to form an aggregate validated set of operating scenarios. The processors further implement a supervisor that, responsive to receipt of target conditions for the zones and the aggregate validated set of operating scenarios from the coordinator, selects a combination of the operating scenarios from the aggregate validated set of operating scenarios that achieves target conditions and minimizes overall energy consumption by the resources such that some of the operating scenarios of the combination do not minimize energy consumption of the resources corresponding to the some of the operating scenarios.

One aspect is directed to a system for controlling airflow in a facility having a ceiling-ducted aisle airflow containment system having a first damper system for controlling airflow. The system includes an input to receive parameters related to airflow in the facility, wherein the parameters include at least one airflow resistance value for a device in the facility, an output to provide output data including at least one setting for one or more controllable devices in the facility, and one or more processors configured to receive the parameters related to airflow, determine airflow values associated with the airflow containment system and based on the airflow values, generate the at least one setting for the one or more controllable devices, including at least one setting for the first damper system.

Described herein are systems and methods for inverse reinforcement learning to leverage the benefits of model-based optimization method and model-free learning method. Embodiments of a framework combining human behavior model with model predictive control are presented. The framework takes advantage of feature identification capability of a neural network to determine the reward function of model predictive control. Furthermore, embodiments of the present approach are implemented to solve the practical autonomous driving longitudinal control problem with simultaneous preference on safe execution and passenger comfort.

The present invention relates to training predictive data-driven model for predicting an industrial time dependent process. A data driven generative model is introduced for modelling and generating complex sequential data comprising multiple modalities, by learning a joint time-dependent representation of the different modalities. The model may be configured to handle any combination of missing modalities, which enables conditional generation based on known modalities, providing a high degree of control over the properties of the generated sequences.

The present invention relates to an MPC-based hierarchical coordinated control method and device for a wind-hydrogen coupling system. The method comprises the following steps: (1) dividing the wind-hydrogen coupling system into upper-layer grid-connected control and lower-layer electrolytic cell control; (2) controlling grid-connected power to track a wind power prediction curve by adopting an MPC control algorithm for upper-layer grid-connected control, and obtaining an electrolytic cell power control quantity for the lower-layer electrolytic cell control at the same time; (3) dividing operation states of electrolytic cell monomers into four operation states of rated power operation, fluctuating power operation, overload power operation and shutdown; and (4) determining the operation states of various electrolytic cell monomers by adopting a time-power double-line rotation control strategy based on the electrolytic cell power control quantity, thus making the electrolytic cell monomers operate in one of the four operating states in turn.

A method for obtaining a product by electrolysis, including: a) determining a set point for a production output by minimizing a first mathematical function, which depends on the production output and on a predicted product demand; b) determining respective set points for multiple process parameters by minimizing a second mathematical function, which depends on the set point for the production output determined in a), on the process parameters and on predicted degradation effects; c) determining respective set points for changes of multiple control parameters by minimizing a third mathematical function, which depends on the set points for the process parameters determined in b) and on the changes of the control parameters; and d) obtaining the product by performing the electrolysis using the set points for the changes of the control parameters determined in c).

Various embodiments described herein relate to management of industrial process optimization related to batch operations. In this regard, an optimization request to optimize an industrial process that produces an industrial process product is received. In response to the optimization request, product spent characteristics for one or more blending components of a batch operation subprocess are determined. Also in response to the optimization request, demand data for one or more feed products associated with the one or more blending components is updated based on the product spent characteristics and inventory data indicative of an inventory level for the one or more feed products. Furthermore, a control signal configured based on the demand data is transmitted to a controller configured for optimization associated with the industrial process that produces the industrial process product.