在土耳其,在能源生产中使用生物质资源的设施正在增加,每年都会启用新的转换设施,以提供环保的能源生产。因此,需要可靠的能源潜力估计。在这项研究中,安塔利亚基于动物粪便的沼气潜力,Isparta,计算了土耳其西地中海地区的Burdur省。这里,关于牛的特殊信息,小反刍动物,家禽,和动物年龄,number,并详细使用了粪肥数量信息。此外,二氧化碳排放,煤炭,电力,和热能,使用1级和2级方法的甲烷排放值通过机器学习算法进行计算和预测。为了确定具有最佳结果的模型,机器学习算法支持向量机(SVM),多层感知器(MLP),使用线性回归(LR),并进行了超参数优化。根据沼气潜力的结果,CO2排放,电力生产,和热能估计SVM模型被视为R2=0.999的最佳模型。当检查煤量估算时,LR模型比SVM和MLP产生更好的结果,R2=0.997。在使用Tier1方法估计CH4时,MLP模型可以执行最佳估计,R2=0.977。在使用Tier2方法获得的CH4建模中,LR模型优于其他模型,性能值R2=0.962。
In Turkey, facilities for the use of biomass resources in energy production are increasing, and new conversion facilities are commissioned every year to provide environmentally friendly energy production. Therefore, reliable energy potential estimates are needed. In this study, the animal manure-based-
biogas potentials of Antalya, Isparta, and Burdur provinces in the Western Mediterranean Region of Turkey were calculated. Here, special information on cattle, small ruminants, and poultry, and animal age, number, and manure amount information were used in detail. In addition, carbon dioxide emissions, coal, electricity, and thermal energy, methane emission values with the Tier 1 and Tier 2 approaches were calculated and predicted by machine learning algorithms. To determine the model with the best results, machine learning algorithms support vector machine (SVM), multi-layer perceptron (MLP), and linear regression (LR) were used, and hyper-parameter optimization was performed. According to the results of
biogas potential, CO2 emission, electricity production, and thermal energy estimations SVM models are seen as the best models with R2 = 0.999. When the coal amount estimation is examined, the LR models produce better results than SVM and MLP with R2 = 0.997. In the estimation of CH4 using the Tier 1 approach, the MLP model can perform the best estimation with R2 = 0.977. In the CH4 modeling obtained using the Tier 2 approach, the LR models were superior to the other models with the performance value of R2 = 0.962.