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In the present paper, a software framework comprising the implementation of Model Predictive Control—a popular industrial control method—is presented. The framework is versatile and can be run on a variety of target systems including programmable logic controllers and distributed control system implementations. However, the main attractive property of the framework stems from the goal of achieving smooth technology transfer from the academic setting to real industrial applications. Technology transfer is, in general, difficult to achieve, because of the apparent disconnect between academic studies and actual industry. The proposed software framework aims at bridging this gap for model predictive control—a powerful control technique which can result in substantial performance improvement of industrial control loops, thus adhering to modern trends for reducing energy waste and fulfilling sustainable development goals. In the paper, the proposed solution is motivated and described, and experimental evidence of its successful deployment is provided using a real industrial plant.

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The main goal of this paper is to identify an industrial water boiler model and design a model predictive controller (MPC). The boiler model was identified from the real process data collected during a heating season. Controller was designed and tested in virtual environment and its performance was compared with performance of classical PI control algorithm that is currently used to control a boiler system. Use of the designed controller leads to significant improvement of accumulated output error.

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