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Economic Research On Phosphogypsum Block Based On Neural Network And E-VIKOR Method

Posted on:2018-07-27Degree:MasterType:Thesis
Country:ChinaCandidate:W J ZhangFull Text:PDF
GTID:2351330518960512Subject:Technical Economics and Management
Abstract/Summary:PDF Full Text Request
Phosphogypsum is inevitable industrial waste residue from the production of phosphate fertilizer and phosphoric acid.Large amounts of phosphogypsum emissions have occupied much land resource and caused severe pollution to environment around.Because of impurities within phosphogypsum itself,unstable structure and low strength after casting,use of phosphogypsum for building gypsum material was limited profoundly.The resource utilization of phosphogypsum can't only reduce environmental harm form phosphogypsum and save natural gypsum,but also result considerable economic benefits.Specific research contents are as follows:(1)Using the superiority of general regression neural network(GRNN)to guide the mix proportion experiments on ? type half water phosphogypsum,compround admixtures and additive materials,the optimal mixture ratio was ultimately determined,with which phosphogypsum building block of the excellent performance was produced.(2)After the market survey on natural gypsum block,referring to production line of it,phosphogypsum building block's cost of production was estimated.(3)The construction process and construction methods of phosphogypsum block were discussed and the consumption of artificial,mechanical and material was measured to determine cost of construction and decoration.(4)Technology,function and economic indicators of phosphogypsum block were summarized to make single index comparion with traditional block material.(5)indicator system in line with building block material was estimated.E-VIKOR fuzzy comprehensive evaluation system was used to select building block material synthetically.Main conclusions are as follows:(1)GRNN used to predict intensity of type building phosphogypsum was established.The fluctuation range and numerical value of error were within the acceptable range.Accurate prediction proved that the nonlinear mapping ability,fault tolerance and self-study ability of GRNN in the proportion experiment on compround admixtures and additive materials were feasible to guide the experiment on the best proportion,which avoided a lot of blind ratio test and waste of resources and improved the experiment level,as well experiment efficiency.(2)According to the strength of GRNN's prediction,water paste ratio of 0.273,0.1%dosage of polyvinyl alcohol,0.7%poly carboxylic acid water reducing agent,5%silica fume,10%ceramsite content were the optimum mixture ratio.phosphogypsum building block's compressive strength range in 12-18MPa,flexural strength in 3-4MPa.(3)When rate of profit was 40%,666*500*100(mm)Phosphogypsum's cost was ? 350.83/m~3 not including tax,slightly higher than traditional block material.Unit price of 100mm phosphogypsum building block' s construction cost was estimated to be Y 482.96/m~3.Prepared with traditional building block,unit price were obvious advantages.(4)The resuilt of block's selecting project by using E-VIKOR was reasonable and credible.In different decision system,the project that all calculated values of phosphogypsum building blocks were minimum was optimal compromising project,which proved new phosphogypsum building block's superiority,economy and boundedness.
Keywords/Search Tags:phosphogypsum block, General Regression Neural Network, optimum mixture ratio, cost, E-VIKOR
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