Abstract
One of the main concerns of industry is energy efficiency, in which the paradigm of Industry 4.0 opens new possibilities by facing optimization approaches using data-driven methodologies. In this regard, increasing the efficiency of industrial refrigeration systems is an important challenge, since this type of process consume a huge amount of electricity that can be reduced with an optimal compressor configuration. In this paper, a novel data-driven methodology is presented, which employs self-organizing maps (SOM) and multi-layer perceptron (MLP) to deal with the (PLR) issue of refrigeration systems. The proposed methodology takes into account the variables that influence the system performance to develop a discrete model of the operating conditions. The aforementioned model is used to find the best PLR of the compressors for each operating condition of the system. Furthermore, to overcome the limitations of the historical performance, various scenarios are artificially created to find near-optimal PLR setpoints in each operation condition. Finally, the proposed method employs a forecasting strategy to manage the compressor switching situations. Thus, undesirable starts and stops of the machine are avoided, preserving its remaining useful life and being more efficient. An experimental validation in a real industrial system is performed in order to validate the suitability and the performance of the methodology. The proposed methodology improves refrigeration system efficiency up to 8%, depending on the operating conditions. The results obtained validates the feasibility of applying data-driven techniques for the optimal control of refrigeration system compressors to increase its efficiency.
Highlights
The Industry 4.0 framework brings the perfect environment in terms of process data availability for artificial intelligence (AI) applications for modelling and monitoring manufacturing systems [1].various efforts to explore the applicability of such models have been developed during recent years [2], with the objective of these approaches being to improve the efficiency of the industrial process by implementing several solutions, such as process monitoring, process fault diagnosis or process energy optimization [3].Among the mentioned researched topics, energy optimization is considered the basis for economic competitiveness and growth [4,5]
The switching management method consists of comparing the expected evolution of the Qcp, provided by the multi-layer perceptron (MLP) at a defined forecasting horizon, with the proposed part load ratio (PLR), resulting in the current Qcp. Such a comparison provides robustness to the decision of switching compressors with the following logic: If the PLR recommended by the methodology and the future trend specified by the MLP are consistent, the switching action can be executed and the PLR suggested by the method is used
The proposed methodology addresses two common challenges that would improve the operation The proposed methodology addresses two common challenges that would improve the of a refrigeration system, using data-driven techniques: (1) the modelling of the system under uncertain operation of a refrigeration system, using data-driven techniques: 1) the modelling of the system operational modes; and (2) the optimal PLR setpoint generation, considering the stability of the under uncertain operational modes; and 2) the optimal PLR setpoint generation, considering the compressors with an appropriate switching management
Summary
The Industry 4.0 framework brings the perfect environment in terms of process data availability for artificial intelligence (AI) applications for modelling and monitoring manufacturing systems [1]. It is crucial to minimize the number of starts and stops of compressors in order to maximize its remaining useful life (RUL) and minimize the energy consumption Another shortcoming found in most data-driven approaches is the inability to obtain robust and optimal setpoint suggestions in scenarios that has not been represented in historical data. Working with two compressors brings the possibility to obtain novel combinations of PLR, never seen in the historical database, which might forth the possibility to obtain novel combinations of PLR, never seen in the historical database, which lead to new near-optimal efficiency curves for a particular process operation. (iv)the how to assure theprocess stabilitywhile of the process while optimizing its efficiency
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