Energy efficiency evaluation and energy saving based on DEA integrated affinity propagation clustering: Case study of complex petrochemical industries

Geng, Zhiqiang; Zeng, Rongfu; Han, Yongming; Zhong, Yanhua; Fu, Hua

doi:10.1016/j.energy.2019.05.042

Cited by 78 publications

(41 citation statements)

References 36 publications

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“…Wu et al proposed a DEA technique with nonhomogeneous variables to estimate the energy efficiency of industrial sectors in China [24]. Geng et al proposed a DEA approach with the affinity propagation clustering algorithm (AP-DEA) to evaluate the energy efficiency of petrochemical industries [25]. However, undesirable outputs, e.g., carbon emissions, are not considered in the aforementioned DEA models.…”

Section: Introductionmentioning

confidence: 99%

Measuring the Energy and Carbon Emission Efficiency of Regional Transportation Systems in China: Chance-Constrained DEA Models

Ren

Gao

Zhang

et al. 2020

Mathematical Problems in Engineering

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In China, the transportation sector contributes about 18% of the total carbon emissions. This research contributes to measuring the energy and carbon emission efficiency (ECEE) of regional transportation systems (RTS) in China considering uncertain carbon emissions. A radial chance-constrained data envelopment analysis (DEA) model is developed to estimate the overall efficiency, and a nonradial chance-constrained DEA model is presented to evaluate the pure energy efficiency (PEE) and the pure carbon emission efficiency (PCEE). We prove that the proposed chance-constrained DEA models can effectively address the uncertain carbon emissions when measuring efficiency. We find that most of China’s RTS have low ECEE and the inefficiencies are mainly due to the lower gasoline utilization efficiency and the lower kerosene utilization efficiency. In addition, east China performs better than central China, and central China performs better than west China. In China, the unbalanced regional development of the ECEE in transportation corresponds with the unbalanced regional economic development. We provide some valuable suggestions based on the evaluation of the potential cuts in each kind of energy and the potential decreases in carbon emissions.

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Section: Introductionmentioning

confidence: 99%

Measuring the Energy and Carbon Emission Efficiency of Regional Transportation Systems in China: Chance-Constrained DEA Models

Ren

Gao

Zhang

et al. 2020

Mathematical Problems in Engineering

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“…The high complexity and uncertainty of energy consumption processes make it preferable to use neural networks to study them [36][37][38][39][40]. In order to expand the mathematical tools for studying the energy efficiency of chemical industries, Jin et al developed approaches to assessing the effectiveness of resource-saving projects based on a backpropagation neural network [41]; Xiang et al proposed an algorithm for learning a multilayer neural network based on photonic spike timing dependent plasticity [42]; Menghi et al systematized methods and tools for energy assessment [43]; Geng et al assessed the energy saving of complex petrochemical industries based on complex clustering of the DEA affinity distribution [44]; Hamedi and Mokhtar applied multivariate linear regression and multilayer perceptronic artificial neural networks to build a baseline for energy consumption in a low-density polyethylene plant [45]; Shinkevich et al showed the advantage of the network model of the value chain for analyzing the resource intensity of production [46]; Rajskaya et al substantiated the expediency of a differentiated approach to assessing the level of innovative development of production resource-saving systems [47].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Energy Consumption in Chemical Production Based on Descriptive Analytics and Neural Network Modeling

et al. 2021

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Improving the energy efficiency of chemical industries and increasing their environmental friendliness requires an assessment of the parameters of consumption and losses of energy resources. The aim of the study is to develop and test a method for solving the problem of optimizing the use of energy resources in chemical production based on the methodology of descriptive statistics and training of neural networks. Research methods: graphic and tabular tools for descriptive data analysis to study the dynamics of the structure of energy carriers and determine possible reserves for reducing their consumption; correlation analysis with the construction of scatter diagrams to identify the dependences of the range of limit values of electricity consumption on the average rate of energy consumption; a method for training neural networks to predict the optimal values of energy consumption; methods of mathematical optimization and standardization. The authors analyzed the trends in the energy intensity of chemical industries with an assessment of the degree of transformation of the structure of the energy portfolio and possible reserves for reducing the specific weight of electrical and thermal energy; determined the dynamics of energy losses at Russian industrial enterprises; established the correlation dependence of the range of limiting values of power consumption on the average rate of power consumption; determined the optimal limiting limits of the norms for the loss of electrical energy by the example of rubbers of solution polymerization. The results of the study can be used in the development of software complexes for intelligent energy systems that allow tracking the dynamics of consumption and losses of energy resources. Using the results allows you to determine the optimal parameters of energy consumption and identify reserves for improving energy efficiency.

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“…7 Gong et al applied AP for determining high influence input data and proposed a DEA model based on the AP to evaluate the EE of an ethylene and PTA production processes. 8 Han et al used AP for obtaining the high impact data affecting the performance capacity and energy saving and presented a DEA crossmodel based on the AP for performance evaluation and energy optimization modeling of a PTA and ethylene production processes. 9 PLS, PCR, and PCA are applied for a dataset with large number of modeling variables and are especially good at addressing the multicollinearity problem where two or more of the features are highly correlated.…”

Section: Introductionmentioning

confidence: 99%

“…The AP clustering algorithm is useful for feature selection in datasets with large deviations and instability 7 . Gong et al applied AP for determining high influence input data and proposed a DEA model based on the AP to evaluate the EE of an ethylene and PTA production processes 8 . Han et al used AP for obtaining the high impact data affecting the performance capacity and energy saving and presented a DEA cross‐model based on the AP for performance evaluation and energy optimization modeling of a PTA and ethylene production processes 9 .…”

Section: Introductionmentioning

confidence: 99%

Steam consumption prediction of a gas sweetening process with methyldiethanolamine solvent using machine learning approaches

2020

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This study proposes a comprehensive data processing and modeling framework for building high-accuracy machine learning model to predict the steam consumption of a gas sweetening process. The data pipeline processes raw historical data of this application and identifies the minimum number of modeling variables required for this prediction in order to ease the applicability and practicality of such methods in the industrial units. On the modeling end, an empirical comparison of most of the state-of-the-arts regression algorithms was run in order to find the best fit to this specific case study. The ultimate goal is to leverage this model to identify the achievable energy conservation opportunity in such plants. The historical data for this modeling was collected from a gas treating plant at South Pars Gas Complex for 3 years from 2017 to 2019. This data gets passed through a multistage data processing scheme that conducts multicollinearity analysis and model-based feature selection. For model selection, a wide range of regression algorithms from different classes of regressor have been considered. Among all these methods, the Gradient Boosting Machines model outperformed the others and achieved the lowest crossvalidation error. The results show that this model can predict the steam consumption values with 98% R-squared accuracy on the holdout test set. Furthermore, the offline analysis demonstrates that there is a potential of 2% energy saving, equivalent to 24 000 metric tons of annual steam consumption reduction, which can be achieved by mapping the underperforming energy consumption states of the unit to the expected performances predicted by the model.

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Energy efficiency evaluation and energy saving based on DEA integrated affinity propagation clustering: Case study of complex petrochemical industries

Cited by 78 publications

References 36 publications

Measuring the Energy and Carbon Emission Efficiency of Regional Transportation Systems in China: Chance-Constrained DEA Models

Measuring the Energy and Carbon Emission Efficiency of Regional Transportation Systems in China: Chance-Constrained DEA Models

Optimization of Energy Consumption in Chemical Production Based on Descriptive Analytics and Neural Network Modeling

Steam consumption prediction of a gas sweetening process with methyldiethanolamine solvent using machine learning approaches

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