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Stepless capacity control technology for reciprocating compressors is a key contributor to energy saving for the petroleum and petrochemical industries. Devices called 'unloaders' are utilized to control the capacity of the compressor by forcibly holding the suction valves open during a variable portion of the compression stroke to control the compressor output. This approach can also lead to various faults of the suction valve. This paper describes the simulation and experimental studies of the transient motion of suction valves under stepless capacity control. Beginning with mathematical models for the normal cycle, improved models of a reciprocating compressor under stepless capacity control have been built. A simulation study of the working process of a double-acting reciprocating compressor has been completed. Theoretical formulas for the transient motion of the valve plate under complicated conditions and the dynamic pressure in the cylinder are compared with the experimental results. Based on the above simulations, a finite element analysis of the valve plate and valve seat has been completed. The experiment results showed that the vibration of the compressor cylinder under complicated conditions was consistent with numerical simulation results. Research presented in this paper is significant in providing tools for diagnosing faults in order to optimize the design of reciprocating compressors that utilize a stepless capacity control system. [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]

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]