Performance Analysis of China Ethylene Plants by Measuring Malmquist Production Efficiency Based on an Improved Data Envelopment Analysis Cross-Model

Han, Yongming; Geng, Zhiqiang; Gu, Xiaoqing; Wang, Zun

doi:10.1021/ie503265w

Cited by 30 publications

(5 citation statements)

References 36 publications

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“…The use of both quantitative and qualitative data was suggested as an option. Han et al (2015) relied on an upgraded data envelopment analysis cross-model to provide more effective decision-making. Sarswatula et al (2022) suggest that the best-performing machine learning algorithm is the Random Forest Regressor as results show high precision, recall, and accuracy.…”

Section: Big Data Analytics Of Energy Consumptionmentioning

confidence: 99%

Analyzing Energy Consumption in Universities: A Literature Review

Alnahhal‬‏,

Antar,

Sakhrieh

et al. 2024

IJEEP

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This study investigates energy consumption in general and in universities in particular. A literature review was used as the methodology of the study to show the energy consumption purposes, major findings, research gaps, limitations, and future research about how to analyze energy consumption levels. The number of references examined was 60, with 29 references about universities and some of them are concentrating on the buildings’ consumption in the GCC area. Most of the references concentrated on investigating case studies in different countries. Analysis of the findings showed that tools such as big data analytics, machine learning, simulations, and predictive models were used. However, big data analytics was the most important one. It was found that AC systems are the most important component of energy consumption, especially in hot weather countries. The studies also showed that monitoring the consumption rates will reduce the total consumption of energy. In universities, the type of the building and the day index were found the most important factors affecting energy consumption. Factors such as number of smart devices, location, and floor space were found to have different positive and negative impacts on energy consumption from one study to another. Recommendations for future research were also presented.

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Section: Big Data Analytics Of Energy Consumptionmentioning

confidence: 99%

Analyzing Energy Consumption in Universities: A Literature Review

Alnahhal‬‏,

Antar,

Sakhrieh

et al. 2024

IJEEP

View full text Add to dashboard Cite

show abstract

“…Sueyoshi and Goto (2014) analyzed energy utilizations and environmental protections of Japanese chemical and pharmaceutical firms by the DEA radial measurement approach. Han et al (2015aHan et al ( , 2015b used the Malmquist production index (MPI) method based on DEA cross-model and fuzzy DEA cross-model to investigate the performance efficiency of Chinese ethylene plants. However, the quantities of input and output indicators and the number of samples had a great influence on the results of DEA analysis (Ruggiero, 2005;Cooper et al, 2006).…”

Section: Literature Reviewmentioning

confidence: 99%

Energy efficiency analysis based on DEA integrated ISM: A case study for Chinese ethylene industries

Han

Geng

et al. 2015

Engineering Applications of Artificial Intelligence

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“…Li et al 6 proposed a multiobjective particle swarm optimization algorithm for the OOP of naphtha pyrolysis process. Han et al 7,8 proposed an improved data envelopment analysis model and a linear optimization fusion model to evaluate the operation efficiency and the energy efficiency of the ethylene plants, respectively. Geng et al 9 designed an adaptive multiobjective particle swarm optimization algorithm to solve the multiobjective OOP (MO-OOP) of ethylenecracking furnace.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Multifactorial Operation Optimization for Continuous Annealing Production Process

Wang

2019

Ind. Eng. Chem. Res.

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To deal with the fluctuations of strip quality, low production capacity, and excessive energy consumption in continuous annealing production line (CAPL), a new multiobjective multifactorial operation optimization (MO-MFOO) model is established for the CAPL in this paper. Different from previous multiobjective operation optimization (MOO) models in literature that only solve one optimization task at a time, the MO-MFOO in this paper can simultaneously solve multiple optimization tasks, each of which is a MOO. To efficiently solve this model, we propose an adaptive multiobjective, multifactorial differential evolution algorithm (AdaMOMFDE), in which multiple mutation operators are adopted and an adaptive selection strategy for these operators is designed according to their search results to accelerate the convergence speed and improve the robustness. Experimental results on benchmark problems show that AdaMOMFDE is better than the traditional multiobjective multifactorial evolutionary algorithm in the literature. Moreover, experimental results on the practical problems illustrate that the MO-MFOO model based on AdaMOMFDE is superior to the traditional multiobjective operation optimization model that only optimizes one task at a time.

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Performance Analysis of China Ethylene Plants by Measuring Malmquist Production Efficiency Based on an Improved Data Envelopment Analysis Cross-Model

Cited by 30 publications

References 36 publications

Analyzing Energy Consumption in Universities: A Literature Review

Analyzing Energy Consumption in Universities: A Literature Review

Energy efficiency analysis based on DEA integrated ISM: A case study for Chinese ethylene industries

Multiobjective Multifactorial Operation Optimization for Continuous Annealing Production Process

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