Energy Consumption Analysis and Prediction of Hot Mix Asphalt

2nd International Symposium on Application of Materials Science and Energy Materials

材料科学;能源学

Huang, Xiaoyu^1 ; Yang, Jianhong^1 ; Fang, Huaiying^1 ; Cai, Yuanyuan^1 ; Zhu, Hejun^1 ; Lv, Ning^1

College of Mechanical Engineering and Automation, Huaqiao University, Xiamen, China^1

关键词: Energy consumption analysis;    Energy consumption and production;    Hot-mix asphalt mixtures;    Influence coefficient;    Kernel principal component;    Kernel principal component analyses (KPCA);    Least squares regression;    Multi-parameter models;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/490/3/032029/pdf
DOI  :  10.1088/1757-899X/490/3/032029

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

Hot mix asphalt (HMA) mixture is a major building material in paving engineering. To decrease energy consumption in HMA, a prediction model of energy consumption was investigated systematically in this paper, employing kernel principal component analysis (KPCA) and a support vector machine (SVM). The purpose of the work is to optimize production and structure parameters of an HMA plant. A prediction model of energy consumption cannot only be used for understanding but can also be used to develop new asphalt mixing plants and to achieve optimization. The main relationships between energy consumption and production parameters are studied in some field tests. To build a multi-parameter model of energy consumption, eigenvectors of many factors are optimized employing KPCA. The three kernel principal components of higher contribution rates are chosen. A prediction model of energy consumption is built using KPCA and SVM. Energy consumption of aggregate drying is predicted using the constructed model. The prediction model is optimized by particle swarm optimization (PSO). Influence coefficients of energy consumption are obtained by SVM and piecewise least-squares regression (PLSR) and are found to be consistent with test results. There is about a 5% error in energy consumption between the model prediction and the test. The prediction error can meet engineering requirements in the hot mix asphalt mixing plant.