Model-predictive energy management system for thermal batch production processes using online load prediction. (July 2022)

• Record Type: Journal Article
• Authors: Fuhrmann, Florian; Schirrer, Alexander; Kozek, Martin
• Is Part Of: Computers & chemical engineering. Volume 163(2022)
• Publication Date: 2022-07
• Subjects: Energy management system -- Model predictive control -- Online load prediction -- Mixed-integer linear programming -- Thermal batch processes; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2022.107830
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: Two-layer model predictive energy management system for industrial batch processes. Modular formulation of the arising mixed-integer linear program with simple parameterization. Online load predictor for thermal batch processes ensuring robustness against uncertainties. Performance demonstration and comparison on an existing food factory. Abstract: Predictive energy management systems (EMS) enable industrial plants to participate in the modern power market and reduce energy cost. In this paper, a novel modular model predictive EMS specifically designed for industrial thermal batch processes is presented. The EMS consists of a two-layer mixed-integer model predictive controller and an online load predictor, and thus solves the main challenges of EMS in industry - high implementation costs and the possible reduction of production reliability. The modular formulation of the optimization problem enables system integrators to implement the EMS without time-consuming modelling tasks and elaborate parameter tuning. The online load predictor estimates the typical pulse-like heat loads of batch processes ensuring both - reliable production and maximal flexibility of the power demand. The utilization of real-time data provides additional robustness against uncertainties caused by human operators. The performance of the EMS is evaluated in a case study of an existing food plant.