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.

中文翻译：

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考虑需求预测的基于数据驱动的工业制冷优化方法

工业最关注的问题之一是能源效率，其中工业4.0的范式通过面对使用数据驱动方法的优化方法而开辟了新的可能性。在这方面，提高工业制冷系统的效率是一个重要的挑战，因为这种类型的过程会消耗大量的电能，而这可以通过最佳的压缩机配置来减少。在本文中，提出了一种新颖的数据驱动方法，该方法使用自组织映射（SOM）和多层感知器（MLP）来处理制冷系统的（PLR）问题。所提出的方法论考虑了影响系统性能的变量，从而建立了工作条件的离散模型。前述模型用于为系统的每个运行条件找到最佳的压缩机PLR。此外，为克服历史性能的局限性，人为地创建了各种方案，以在每种操作条件下找到接近最佳的PLR设定点。最后，提出的方法采用预测策略来管理压缩机的切换情况。因此，避免了机器的不期望的启动和停止，从而保留了其剩余的使用寿命并且更加有效。为了验证方法的适用性和性能，在实际的工业系统中进行了实验验证。所提出的方法可将制冷系统效率提高多达8％，具体取决于运行条件。