Energy Efficiency Evaluation Method And Application In Ethylene Industry

Energy is the driving force for the development of the national economy, the significant support of modern economy, and the blood of modern industry. With the increasing application of energy, the problem of environment is more and more prominent. Thus, energy conservation and emissions reduction has become a national strategy. Process industry is one of the largest energy consumption industries in economic production, while the ethylene industry is one of the largest energy consumption process systems in process industry. The energy efficiency analysis and energy-saving evaluation on ethylene industry are the key technologies to solve its energy conservation and emissions reduction, as well as an important research and application field of control science and engineering.Because the composition of the factors, operation more complicated structures, energy flow, material flow and information flow cross coupling at the same time, it is difficult to establish an accurate model energy efficiency evaluation of ethylene industry. Moreover, a single model cannot describe the dimensions, the component factors and the correlation between each other of the energy transfer system in a reasonable, comprehensive and clear way. Due to above characteristics, this paper proposes an energy efficiency evaluation method applied to the process industry. Through the application on ethylene industry, the results verify the correctness, reliability and practicability of the proposed method. The main research contents and results are as follows:(1) The data envelopment analysis (DEA) model has the problem of mutual restraint between the input-output and decision-making units (DMUs), and its input-output indexes cannot be too many. To solve these problems, the DEA model energy efficiency analysis and hierarchy weighted fusion algorithm for DEA energy efficiency data based on input-output data clustering analysis is proposed. Researching on clustering analysis based on input-output data, and then different types of devices are evaluated by DEA energy efficiency to find its different energy efficiency conditions. Meanwhile, the hierarchy weighted fusion analysis of the DEA efficiency data not only can make up the inadequacy of DEA in itself, but also can make hierarchy analysis of energy efficiency of month, year and the whole industry. The results kindly reflect the energy efficiency hierarchy structure of process industry.(2) DEA model itself can only distinguish the pros and cons of ethylene plant energy efficiency, but not achieve the sort of effective decision-making units, and self-evaluation model in typical DEA cross model cannot obtain input-output improved value of non-effective decision-making unit, and connot dynamically analyze process plant energy efficiency situation. To solve the above drawbacks, the MALMQUIST index (MPI) energy efficiency evaluation method based on the improved DEA cross model is proposed. The improved DEA cross model introduced slack variables to the self-evaluation model, which can find input-output improved value of non-effective decision-making units, and its cross model achieve the sort of effective decision-making units. The MALMQUIST efficiency index based on improved DEA cross model is applied to analyze ethylene industry situation factors, which can guide the improvement direction of ethylene energy efficiency from technology improvement, technical efficiency and scale efficiency etc.(3) Because the process industry has features of many energy consumption data and part of the data with fuzzy uncertainty, classical clustering analysis and hierarchical analysis algorithms cannot effectively analyze fuzzy uncertain data. The energy efficiency evaluation method based on fuzzy fusion algorithm is proposed. The proposed fuzzy C-means linear optimization algorithm can avoid the disadvantage that weight value is negative, improving the accuracy of data fusion. Meanwhile, fuzzy hierarchy fusion algorithm can help the research ot fuzzy data visualization, and further optimize ethylene industry energy efficiency factor to provide clear improvement direction for the energy efficiency level of ethylene plants in specific application of ethylene plants.(4) The interpretative structural model(ISM) based on energy efficiency can easily divide associated factors of energy efficiency into different levels, and establish the primary and secondary relationship of the energy efficiency factors. Therefore energy efficiency evaluation method of the ISM based on partial correlation coefficient is proposed. The ISM based on partial correlation coefficient can obtain correlation among factors based on the data related to energy efficiency factors, effectively building energy efficiency analysis hierarchy. The proposed method can be used to define the primary and secondary relationship among factors affecting ethylene industrial energy efficiency, and guide the improvement direction of the production process energy efficiency, and improve the utilization rate of ethylene production input-output.The ethylene production process energy consumption system, as a large energy consumption of process industry, is taken as an example of this paper. This paper researches the layer-layer depth evaluation methods based on data envelopment analysis (DEA), fuzzy hierarchy fusion analysis, and interpretative structural model etc., establishing a multi-level and multi-dimensional energy efficiency evaluation method database, which can help find the direction and quantitative targets of ethylene plant energy savings, avoid increasing the cost of energy consumption to pursue product quality, guide the improvement direction of ethylene green production and improve ethylene energy efficiency. Meanwhile, it has an applicable value and reference significance for the other plant production of process industry.