Energy saving diagnosis model of petrochemical plant based on intelligent curvelet support vector machine

Abstract

The energy and resources saving has become a major task of petrochemical enterprises, it is necessary to construct the energy saving diagnostic system for understand the real time operation information of petrochemical plant and provide theoretical basis for taking energy saving measures. The energy saving diagnosis process of petrochemical plant based on twin Curvelet support vector machine optimized by hybrid glowworm swarm algorithm is designed. The Curvelet kernel function is constructed based on curvelet transform to establish theory model of twin curvelet support vector machine. In order to improve the prediction precision of the twin curvelet support vector machine, the hybrid glowworm swarm optimization algorithm is constructed based on simulated annealing simulation to optimize the parameters of the twin Curvelet support vector machine. Finally, a petrochemical plant is used as research object to carry out diagnosis simulation analysis, and results showed that the proposed prediction model can effectively improve diagnostic effectiveness of the energy saving effect of petrochemical plant.

A novel approach for the detection of anomalous energy consumption patterns in industrial cyber‐physical systems

“…In B. Zhao, Qin, Gao, and Xu et al (2021) the authors design an energy saving diagnosis process in a petrochemical plant based on the dual Curvelet support vector machine (SVM). To improve the accuracy and optimize the hyper parameters of the SVM, they construct a hybrid glowworm swarm optimisation algorithm based on simulated annealing to optimize the parameters of the twin Curvelet SVM.…”

“…To begin with, in practice energy inefficiencies depend on the overall dynamics of the cyber‐physical system, and facing their detection from individual machine level or from aggregated factory level, they have in common that they only partially capture the state of the environment Eiteneuer and Niggemann (2020). On the other hand, so far, a large number of heterogeneous machine learning algorithms have been used in inefficiency detection, for example, the extraction of significant features using SOMs is proposed Calvo‐Bascones et al (2021) and the use of clustering algorithms for pattern identification Hranisavljevic et al (2020) or the treatment of time series for value prediction and study of residuals is proposed Eiteneuer and Niggemann (2020); Feng and Tian (2021) or models that reconstruct compressed data Eiteneuer et al (2019) or support vector machine algorithms B. Zhao et al (2021). However, they only focus on detecting the anomalies for which they were trained and which correspond to excessive energy use Himeur et al (2021), and do not usually provide information on what was the root cause that originated such anomaly.…”

Surface Deformation Prediction Model of High and Steep Open-Pit Slope Based on APSO and TWSVM

“…The artificial intelligence model is suitable for the construction of complex nonlinear models, which significantly improves the accuracy of slope prediction [4]. Artificial intelligence models include the random decision forest model [5], the neural network model [6], the support vector machine [7], the extreme learning machine [8], etc. However, when the random decision forest model has a large number of features or contains a large number of decision trees, the system is prone to overfitting [5], and the neural network model is prone to overfitting and local optimal solutions during operation [9].…”