As one of the main costs of industrial processes, energy consumption is an essential issue that cannot be ignored in the sustainable development of enterprises. Effective energy efficiency evaluation is important, but also challenging. The conventional method typically uses statistic standard for evaluation. However, infected by the market, the raw materials of an industrial process often fluctuate, which will affect the evaluation standard of energy efficiency. Taking this into account will improve the evaluation performance. To this end, a real-time dynamic energy efficiency evaluation and analysis method based on multi-objective state transition algorithm with reference vector (RV-MOSTA) is proposed to address the energy efficiency evaluation problem. The core of the paper is to realize the dynamic energy efficiency evaluation of multiple indicators considering inlet conditions of industrial processes. Therefore, a systematic set of special indicators are firstly developed for the energy efficiency evaluation. Then, RV-MOSTA is proposed to determine the evaluation standard, which considers the influence of imported inlet conditions and the optimization of multiple evaluation indicators. Furthermore, a degeneration diagnosis method by means of linear discriminant analysis (LDA) can identify the key variables that lead to energy efficiency degeneration. The proposed method is verified through numerical example and an industrial hydrocracking process.

中文翻译：

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基于参考向量的多目标状态转移算法的工业过程实时动态能效评价与分析

作为工业过程的主要成本之一，能源消耗是企业可持续发展不可忽视的重要问题。有效的能效评估很重要，但也具有挑战性。传统方法通常使用统计标准进行评估。但受市场影响，一个工业过程的原材料经常出现波动，这会影响能源效率的评价标准。考虑到这一点将提高评估性能。为此，针对能效评价问题，提出了一种基于参考向量多目标状态转移算法（RV-MOSTA）的实时动态能效评价分析方法。论文的核心是实现考虑工业过程入口条件的多指标动态能效评价。因此，首先制定了一套系统的专门指标用于能源效率评价。然后，提出RV-MOSTA确定评价标准，考虑进口进口条件的影响和多项评价指标的优化。此外，通过线性判别分析（LDA）的退化诊断方法可以识别导致能源效率退化的关键变量。通过数值例子和工业加氢裂化过程验证了所提出的方法。首创了一套系统化的能效评价专项指标。然后，提出RV-MOSTA确定评价标准，考虑进口进口条件的影响和多项评价指标的优化。此外，通过线性判别分析（LDA）的退化诊断方法可以识别导致能源效率退化的关键变量。通过数值例子和工业加氢裂化过程验证了所提出的方法。首创了一套系统化的能效评价专项指标。然后，提出RV-MOSTA确定评价标准，考虑进口进口条件的影响和多项评价指标的优化。此外，通过线性判别分析（LDA）的退化诊断方法可以识别导致能源效率退化的关键变量。通过数值例子和工业加氢裂化过程验证了所提出的方法。通过线性判别分析（LDA）的退化诊断方法可以识别导致能源效率退化的关键变量。通过数值例子和工业加氢裂化过程验证了所提出的方法。通过线性判别分析（LDA）的退化诊断方法可以识别导致能源效率退化的关键变量。通过数值例子和工业加氢裂化过程验证了所提出的方法。