| Over the past four decades,China's economy has experienced high speed development since its policy of reforming and opening[19].However,pure pursuit of economic interests leads to the fact that depletion of natural re-sources,carbon emission,low efficiency and severe pollution have become severe issues restricting China's economic development[1],which are dramatically prominent in China's chemical industry.The ever-growing awareness of environment and energy protection has significantly influenced the conventional high pollution,high energy consuming chemical industry in China.In order to promote energy saving and emissions reduction in China's chemical industry,sustainable chemical industry is viewed as an abatement with great potential.Petrochemical and chemical industry "Thirteenth five-year"plan emphasizes the green sustainable development in China's chemical industry.The concept of sustainable development emphasizes the simultaneous consideration of economic,social and environmental factors.However,most of the traditional chemical evaluation methods are economically oriented,ignoring environmental and social factors;or even considering environmental and social factors,they do not consider the contradictory characteristics of the three.Studies have shown that bipolar fuzzy sets compare well with other fuzzy sets.In view of this,this paper is mainly based on bipolar fuzzy set theory,and gives a series of bipolar analysis methods that integrates fuzzy cognitive maps,decision trees,and deep-seated lyrical learning for sustainable chemical evaluation.The main innovations of this article are as follows:1.Using bipolar fuzzy technology,conflict information is fully considered in the evaluation of sustainable chemical analysis.2.The fuzzy cognative map and decision tree are extended to bipolar,and the results of sustainable chemical decision-making and decision-making process with conflict information are analyzed.3.Extend deep learning technology in public opinion analysis to bipolarity and successfully apply information and data obtained from network big data to negative environmental assessments such as chemical environment and society.This solves the problem of difficult access to negative data that has plagued sustainable chemical evaluation.Through example analysis and comparison,it is proved that the proposed algorithm is not only feasible but also effective.More importantly,the algorithm proposed in this paper is more in line with the current concept of sustainable development of environmental and energy protection,and provides new ideas for the sustainable development of China's chemical industry. |
PDF Full Text Request