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Modeling of yield and environmental impact categories in tea processing units based on artificial neural networks

机译:基于人工神经网络的茶处加工单位产量与环境影响等级建模

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In this study, an artificial neural network (ANN) model was developed for predicting the yield and life cycle environmental impacts based on energy inputs required in processing of black tea, green tea, and oolong tea in Guilan province of Iran. A life cycle assessment (LCA) approach was used to investigate the environmental impact categories of processed tea based on the cradle to gate approach, i.e., from production of input materials using raw materials to the gate of tea processing units, i.e., packaged tea. Thus, all the tea processing operations such as withering, rolling, fermentation, drying, and packaging were considered in the analysis. The initial data were obtained from tea processing units while the required data about the background system was extracted from the EcoInvent 2.2 database. LCA results indicated that diesel fuel and corrugated paper box used in drying and packaging operations, respectively, were the main hotspots. Black tea processing unit caused the highest pollution among the three processing units. Three feed-forward back-propagation ANN models based on Levenberg-Marquardt training algorithm with two hidden layers accompanied by sigmoid activation functions and a linear transfer function in output layer, were applied for three types of processed tea. The neural networks were developed based on energy equivalents of eight different input parameters (energy equivalents of fresh tea leaves, human labor, diesel fuel, electricity, adhesive, carton, corrugated paper box, and transportation) and 11 output parameters (yield, global warming, abiotic depletion, acidification, eutrophication, ozone layer depletion, human toxicity, freshwater aquatic ecotoxicity, marine aquatic ecotoxicity, terrestrial ecotoxicity, and photochemical oxidation). The results showed that the developed ANN models with R-2 values in the range of 0.878 to 0.990 had excellent performance in predicting all the output variables based on inputs. Energy consumption for processing of green tea, oolong tea, and black tea were calculated as 58,182, 60,947, and 66,301 MJ per ton of dry tea, respectively.
机译:在这项研究中,开发了一种人工神经网络(ANN)模型,用于预测基于在伊朗桂兰省省省茶,绿茶和乌龙茶茶中加工所需的能量投入的产量和生命周期环境影响。使用寿命周期评估(LCA)方法用于根据摇篮进入浇口方法,即,从使用原材料到茶处加工单元的浇口,即包装茶叶的输入材料的生产茶叶的环境影响类别。因此,在分析中考虑了所有茶处处理操作,例如搅拌,轧制,发酵,干燥和包装。初始数据是从茶处处理单元获得的,而从生态识别2.2数据库中提取有关背景系统的所需数据。 LCA结果表明,在干燥和包装操作中使用的柴油燃料和瓦楞纸盒是主要热点。黑茶处加工单元在三个加工单位之间引起了最高的污染。基于Levenberg-Marquardt训练算法的三个前馈回传播ANN模型,其中有两个隐藏层伴有Sigmoid激活功能和输出层的线性传递功能,用于三种加工茶。神经网络是基于八种不同输入参数的能量等同物(新鲜茶叶,人工,柴油燃料,电力,粘合剂,纸箱,瓦楞纸盒和运输)的能量等价物(能量等价物)和11个输出参数(产量,全球变暖,非生物耗竭,酸化,富营养化,臭氧层枯竭,人类毒性,淡水生态毒性,海洋生态生态毒性,陆地生态毒性和光化学氧化)。结果表明,在0.878至0.990范围内的具有R-2值的发达的ANN模型在预测基于输入的所有输出变量方面具有出色的性能。用于加工绿茶,乌龙茶和红茶的能耗分别计算为58,182,60,947和66,301MJ,每吨干茶。

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