A novel DEACM integrating affinity propagation for performance evaluation and energy optimization modeling: Application to complex petrochemical industries

Han, Yongming; Long, Chang; Geng, Zhiqiang; Zhu, Qunxiong; Zhong, Yanhua

doi:10.1016/j.enconman.2018.12.120

Cited by 39 publications

(17 citation statements)

References 39 publications

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“…22 Han et al also proposed a novel data envelopment analysis (DEA) cross model based on the affinity propagation clustering algorithm to save energy and decrease carbon emission. 23 From the above literature review, it can be seen that energy consumption or production optimization at the plant or equipment level is the main research direction. However, in practical production, energy efficiency is a more comprehensive indicator than energy consumption or production.…”

Section: Related Workmentioning

confidence: 99%

“…Han et al presented a production capacity analysis and energy optimization model of the ethylene and purified terephthalic acid production systems in complex petrochemical industries based on a novel extreme learning machine integrating affinity propagation clustering . Han et al also proposed a novel data envelopment analysis (DEA) cross model based on the affinity propagation clustering algorithm to save energy and decrease carbon emission . From the above literature review, it can be seen that energy consumption or production optimization at the plant or equipment level is the main research direction.…”

Section: Introductionmentioning

confidence: 99%

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Energy efficiency optimization of ethylene production process with respect to a novel FLPEM‐based material‐product nexus

2019

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Summary Energy efficiency optimization and energy management can bring multiple economic benefits for the complicated petrochemical industries. However, the influence of the production scale and materials, and the uncertain data have a direct impact on the production prediction and energy efficiency optimization of ethylene production process, which is ignored by the conventional optimization strategies. To achieve energy management and reduce energy consumption considering the production technology, total energy, and material flows, a multiobjective energy efficiency optimization scheme for the ethylene production process with respect to material‐product nexus is proposed. Specifically, a total‐factor material‐product nexus model of the ethylene production is established by a novel functional link prediction error method–integrated algorithm, providing system‐level material equilibrium constraints for the energy efficiency optimization scheme. Then an optimization model with two mutually complementary energy efficiency indicators is established and implemented by the multiobjective particle swarm optimization integrating functional link prediction error method. The proposed energy efficiency optimization scheme is applied into a practical ethylene plant, and the optimization results demonstrate that energy efficiency of this plant is increased by 10.30%, synthetical energy consumption is decreased by 13.05%, and energy cost is decreased by 10.42%.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy efficiency optimization of ethylene production process with respect to a novel FLPEM‐based material‐product nexus

2019

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“…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. 10 Zhu and Chen utilized PLS to identify the most significant variables and proposed an EE evaluation and prediction method integrated with the Gaussian process and PLS in an ethylene production unit.…”

Section: Introductionmentioning

confidence: 99%

“…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 . 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 10 .…”

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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“…Three different prediction models, linear, exponential, and quadratic are considered, and the predictive variables are economic growth, population, economic structure, urbanization rate, energy structure, and energy price. Moreover, there are other techniques in, where different mixtures between clustering and neural networks in order to predict energy consumption have really good performance applied to industry problems. In, a problem of energy demand future projection has been considered, and in, the energy demand forecasting in the primary sector has been considered.…”

Section: Introductionmentioning

confidence: 99%

Particle swarm grammatical evolution for energy demand estimation

Martínez‐Rodríguez

Colmenar

Hidalgo

et al. 2019

Energy Science & Engineering

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Grammatical Swarm is a search and optimization algorithm that belongs to the more general Grammatical Evolution family, which works with a set of solutions called individuals or particles. It uses the Particle Swarm Optimization algorithm as the search engine in the evolution of solutions. In this paper, we present a Grammatical Swarm algorithm for total energy demand estimation in a country from macroeconomic variables. Each particle in the Grammatical Swarm encodes a different model for energy demand estimation, which will be decoded by a predefined grammar. The parameters of the model are also optimized by the proposed algorithm, in such a way that the model is adjusted to a training set of real energy demand data, selecting the more appropriate variables to appear in the model. We analyze the performance of the Grammatical Swarm evolution in two real problems of one‐year ahead energy demand estimation in Spain and France. The proposal is compared with previous approaches with competitive results.

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A novel DEACM integrating affinity propagation for performance evaluation and energy optimization modeling: Application to complex petrochemical industries

Cited by 39 publications

References 39 publications

Energy efficiency optimization of ethylene production process with respect to a novel FLPEM‐based material‐product nexus

Energy efficiency optimization of ethylene production process with respect to a novel FLPEM‐based material‐product nexus

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

Particle swarm grammatical evolution for energy demand estimation

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