Determining input variable ranges given a trained regression model and an output range Industrial process control systems try to keep an output variable within a given tolerance around a target value. PID control systems have been widely used in industry to control input variables in order to reach this goal. However, this kind of Transfer Function based approach cannot be extended to complex processes where input data might be nonnumeric, high dimensional, sparse, etc. In such cases, there is still a need for determining the subspace of input data that produces an output within a given range. This paper presents a nonstochastic approximation to determine input values for a mathematical function or trained regression model given an output range. The proposed method creates a synthetic training data set of input combinations with a class label that indicates whether the output is within the given target range or not. Then, a decision tree classifier is used to determine the subspace of input data of interest. This method is more general than a traditional controller as the target range for the output does not have to be centered around a reference value and it can be applied given a regression model of the output variable, which may have categorical variables as inputs and may be high dimensional, sparse... The proposed method is validated with a proof of concept on a real use case where the quality of a lamination factory is established to identify the suitable subspace of production variable values.

Timeminimal set point transition for nonlinear SISO systems under different constraints Abstract   Set point transition of nonlinear plants plays an important role in many applications where dynamic process management has to be considered. This transition should be rapid xe2x80x93 as operation in the new set point increases the economical benefit xe2x80x93 but in compliance with all safety regulations. We present a feedforward approach for a timeminimal set point transition of singleinput, singleoutput nonlinear plants with respect to input, state and output constraints. The conceptual idea is based on the design of an optimization problem utilizing a coordinate change of the plant and a special setup function for the output trajectory. This allows the simultaneous planning of the trajectory and determination of the corresponding control signal. The formulation of the set point transition as an optimization problem provides a flexible design with respect to the integration of inequality constraints. Additional flexibility is provided by the type of setup functions, which permit any adjustment of the output trajectory between the set points. In contrast to other works, we focus on the stationarity of the system output, which allows a faster transition compared to the requirement to reach a steady state of all states. Finally, we present an example from the field of process systems engineering to demonstrate the applicability of the proposed methodology.