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One of the key issues of the sustainable development in all countries is industrial productivity improvement, especially the production capacity improvement and energy-saving of complex chemical processes. Therefore, this paper proposes a production prediction and energy-saving model based on Extreme Learning Machine (ELM) integrated Interpretative Structural Modeling (ISM) and Analytic Hierarchy Process (AHP). The factors that affect the productivity are divided into different levers by the ISM. And then the attributes of each layer are fused by the AHP based on the entropy weight, which greatly reduces the complexity of the input attributes. Moreover, the production prediction and energy-saving model is established based on the ELM. Compared with the traditional ELM, the validity and the practicability of the proposed method are verified by University of California Irvine (UCI) datasets. Finally, the proposed method is applied in the production capacity prediction and energy-saving of ethylene production systems and Purified Terephthalic Acid (PTA) production systems. The experimental results show the proposed method could reduce the number of hidden layer nodes and improve the training time of the ELM. Furthermore, the prediction accuracy of the ethylene production and the PTA production reaches about 99% to improve the energy efficiency of complex chemical processes. [ABSTRACT FROM AUTHOR]

DEA (data envelopment analysis) has been widely used for the efficiency analysis of industrial production process. However, the conventional DEA model is difficult to analyze the pros and cons of the multi DMUs (decision-making units). The DEACM (DEA cross-model) can distinguish the pros and cons of the effective DMUs, but it is unable to take the effect of the uncertainty data into account. This paper proposes an efficiency analysis method based on FDEACM (fuzzy DEA cross-model) with Fuzzy Data. The proposed method has better objectivity and resolving power for the decision-making. First we obtain the minimum, the median and the maximum values of the multi-criteria ethylene energy consumption data by the data fuzzification. On the basis of the multi-criteria fuzzy data, the benchmark of the effective production situations and the improvement directions of the ineffective of the ethylene plants under different production data configurations are obtained by the FDEACM. The experimental result shows that the proposed method can improve the ethylene production conditions and guide the efficiency of energy utilization during ethylene production process. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • A novel AP-DEACM method is proposed. • Performance evaluation and energy optimization model is obtained. • This proposed model is efficient in energy saving and carbon emission reduction. • The energy saving potential of ethylene and PTA production plants are 2.78% and 1.26%. • Carbon emission reduction potential is 3.62% in ethylene production plants. Abstract Data Envelopment Analysis (DEA) has been widely used in performance and energy efficiency evaluation. However, in the traditional DEA, the effective of each decision making unit (DMU) is evaluated through its own optimized perspective and regardless of other DMUs influence, which may result in too many effective DMUs. And the DEA cross-model (DEACM) can distinguish the effective DMUs better by constructing a cross-efficiency matrix, but the optimal weight of the DMU may not be unique, so the cross efficiency of the DEACM may be different. Therefore, this paper proposes a novel DEACM based on the affinity propagation (AP) clustering algorithm (AP-DEACM). Through the AP clustering algorithm, the high impact data affecting the performance capacity and energy saving are obtained. Then the better effective DMU is identified through the high discrimination of the AP–DEACM. Finally, the proposed AP-DEACM is used for performance evaluation and energy optimization modeling of the Pure Terephthalic Acid (PTA) production process and the ethylene industrial process in complex petrochemical industries. The experimental results show that the energy saving potential of PTA production plants and ethylene production plants are 2.78% and 1.26%, respectively, and the average value of carbon emission savings potential is 3.62% in ethylene production plants. [ABSTRACT FROM AUTHOR]

Abstract Nowadays, petrochemical industries with many integrated units and equipment have characteristics of high uncertainty and nonlinearity. Therefore, it becomes more and more difficult to make reliable and accurate point measurement of energy modeling. To tackle this problem, a novel prediction intervals (PIs) method integrating error & self-feedback extreme learning machine (ESF-ELM) with particle swarm optimization (PSO) is proposed. For improving the energy modeling accuracy of extreme learning machine (ELM), the input weights are initialized using cosine similarity coefficients but not randomly initialized. In addition, an error-feedback layer and a self-feedback layer are added to the input layer and the hidden layer for enhancing generalization performance, respectively. Finally, PSO with a comprehensive measure is developed to evaluate the mean coverage probability and the mean width percentage of PIs. The proposed ESF-ELM with PSO is applied to constructing PIs of energy consumption for a Purified Terephthalic Acid production process. Simulation results show the proposed model can generate high-quality PIs with large coverage probability, narrow width, and superiority in adaptability and reliability, which provides guidance for decision makers to maximize benefits and give reasonable future plans. Highlights • A novel effective energy consumption prediction intervals method is proposed. • An Error feedback and a self-feedback layer are added to extreme learning machine. • Prediction intervals are optimized by using particle swarm optimization. • A case study of Purified Terephthalic Acid is executed for performance validation. • Simulation results show the proposed method can obtain good prediction intervals. [ABSTRACT FROM AUTHOR]

Energy management and optimization play a key effect in the sustainable development. However, the uncertain data has a direct impact on the production prediction and energy optimization of complex petrochemical industries. Therefore, this paper proposes a novel energy management and optimization model based on the fuzzy extreme learning machine (FELM) method integrated the fuzzy set theory. The minimal, the median and the maximal values of the energy consumption data are obtained by data fuzzification to solve the problem of the fluctuation and uncertainty data. And the cross recombination of triangular fuzzy numbers (TFNs) is applied in the training of the FELM. Moreover, the upper and the lower limits of efficiency values are obtained on the basis of the network generalization to analyze the energy conservation and saving potentials. Furthermore, the FELM has better predictive performance and training speed than fuzzy error back propagation network (FBP) and fuzzy radical basis function network (FRBF) though University of California Irvine (UCI) standard datasets. Finally, the proposed method is applied to manage and optimize the energy status of China ethylene industry in complex petrochemical industries. The experimental results show that the proposed method is effective and applicable in the energy-saving potential, which is indicated up to about 15%. [ABSTRACT FROM AUTHOR]

Energy saving and management of complex chemical processes play a crucial role in the sustainable development procedure. In order to analyze the effect of the technology, management level, and production structure having on energy efficiency and energy saving potential, this paper proposed a novel integrated framework that combines index decomposition analysis (IDA) with energy saving potential method. The IDA method can obtain the level of energy activity, energy hierarchy and energy intensity effectively based on data-drive to reflect the impact of energy usage. The energy saving potential method can verify the correctness of the improvement direction proposed by the IDA method. Meanwhile, energy efficiency improvement, energy consumption reduction and energy savings can be visually discovered by the proposed framework. The demonstration analysis of ethylene production has verified the practicality of the proposed method. Moreover, we can obtain the corresponding improvement for the ethylene production based on the demonstration analysis. The energy efficiency index and the energy saving potential of these worst months can be increased by 6.7% and 7.4%, respectively. And the carbon emissions can be reduced by 7.4–8.2%. [ABSTRACT FROM AUTHOR]

Ethylene production shares the common characteristics of different techniques and scales, high dimension and multi-correlation. However, the existing methods do not take the influence of the technology, scale and materials into account. In order to analyze the energy efficiency of ethylene plants better, this paper proposes a linear optimization fusion model based on fuzzy C-means (FCM) clustering. The FCM clustering is used to obtain the corresponding classification and clustering centers of the large scale time series data of the ethylene production process. Then the mean squares of time-series variables are calculated by various clustering centers. The multidimensional energy efficiency benchmarking of ethylene plants can be extracted by the fusion weighting vectors in linear optimization fusion model. The experimental results indicate that the proposed method intuitively reveals the running state and the operating level of ethylene plants and accurately identify the reason of energy consumption for improving energy efficiency. [ABSTRACT FROM AUTHOR]

Extreme learning machine (ELM), which is a simple single-hidden-layer feed-forward neural network with fast implementation, has been widely applied in many engineering fields. However, it is difficult to enhance the modeling ability of extreme learning in disposing the high-dimensional noisy data. And the predictive modeling method based on the ELM integrated fuzzy C-Means integrating analytic hierarchy process (FAHP) (FAHP-ELM) is proposed. The fuzzy C-Means algorithm is used to cluster the input attributes of the high-dimensional data. The Analytic Hierarchy Process (AHP) based on the entropy weights is proposed to filter the redundant information and extracts characteristic components. Then, the fusion data is used as the input of the ELM. Compared with the back-propagation (BP) neural network and the ELM, the proposed model has better performance in terms of the speed of convergence, generalization and modeling accuracy based on University of California Irvine (UCI) benchmark datasets. Finally, the proposed method was applied to build the energy saving and predictive model of the purified terephthalic acid (PTA) solvent system and the ethylene production system. The experimental results demonstrated the validity of the proposed method. Meanwhile, it could enhance the efficiency of energy utilization and achieve energy conservation and emission reduction. [ABSTRACT FROM AUTHOR]

The petrochemical industry evaluation is affected by numerous factors. Many previous studies proposed a use of data envelopment analysis (DEA) as a methodology for energy efficiency analysis in the petrochemical industry. However, excessive decision-making units (DMUs) of DEA model result in difficulties in evaluation and comparison of the different DMUs. In this paper, a new energy analysis framework of petrochemical industrial processes based on DEA integrated interpretative structural model (ISM) is proposed. The ISM method is brought up based on the partial correlation coefficient method to find the main factors and basic reasons that affect the energy consumption of the ethylene production system, which serve as the inputs of the DEA. Meanwhile, ethylene, propylene and C4 productions of the ethylene production system sever as the outputs of the DEA. Then the fractional DEA model is solved by using the linear programming method. The proposed evaluation method can overcome the shortcomings of the DEA model mentioned above, and also is able to reflect the effectiveness of the DMUs and guide the improvement directions of the ineffective DMUs based on slack variables. Our approach is applied in the energy efficiency analysis of Chinese ethylene industry in the petrochemical field. The empirical results show that the proposed energy consumption analysis method is valid and efficient in improvements of energy efficiency in ethylene production systems. [ABSTRACT FROM AUTHOR]

As the most effective indicator for energy saving and emission reduction, energy efficiency evaluation is widely used in complex petrochemical industries. It is nowadays common to combine traditional mechanism methods based on momentum transport, energy transport, quality transport (TT) and reaction engineering (RG) (TT-RG), with data-driven artificial intelligence methods. Using the combined method to achieve production optimization and energy saving by analyzing the evaluation indicator of energy efficiency has gradually become an important part in complex petrochemical industries. Therefore, this paper introduced the main methods and the latest research results of energy efficiency evaluation of complex petrochemical industries. These methods are mainly divided into three parts, including the mechanism methods based on TT-RG, the data-driven artificial intelligence methods, and the hybrid methods combining the mechanism and the data-driven. Then, different methods are compared and described in detail. Moreover, the best method for evaluating the energy efficiency can be found to provide theoretical guidance for energy saving and emission reduction of complex petrochemical industries. Finally, the future development direction for energy efficiency evaluation in complex petrochemical industries is given. Image 1 • Energy efficiency evaluation methods of petrochemical industries are reviewed and compared. • The latest research results of energy efficiency evaluation of petrochemical industries are analyzed. • The development direction of energy efficiency evaluation of petrochemical industries are given. [ABSTRACT FROM AUTHOR]