Regression prediction of the energy consumption of air source heat pump drying based on machine learning

In order to reduce the energy consumption of air source heat pump drying, a prediction method of energy consumption was proposed to optimize the drying process by means of multivariate linear regression model (MLRM) and back propagation neural network (BPNN) model. On the basis of analyzing characteristics of the energy consumption and factors that affect it, the drying process was proposed to split into sections of equal time to reduce the difficulty of data acquisition. Eight characteristic parameters were set as input, and two parameters were set as output. Input parameters were set temperature in drying room, set humidity in drying room, initial temperature in drying room, initial humidity in drying room, mean ambient temperature, mean ambient humidity, weight of material, and initial moisture content of material. Output parameters were energy consumption and end moisture content of material. Before build predictive models, the variation of the drying power of the air source heat pump was analysed. From the air source heat pump control principle and the drying power curve, the drying process was mainly divided into a heating stage and a insulation stage. The insulation stage consisted of a number of circulating insulation units. As the weight of pea increased, the duration of the heating stage increased, the duration of the insulation stage shortened. Meanwhile, the time of the insulation unit increased and the total drying time increased. Orthogonal experiment was designed using unit energy consumption (energy consumption required to reduce the moisture content of 1 kg of material by 1%) and total energy consumption as the evaluation indexes. According to the results of orthogonal experiment, among the three influencing factors of weight of pea, set temperature in drying room, and set humidity in drying room, the weight of pea had the most significant influence on unit energy consumption and total energy consumption, and the set humidity in drying room had slight significant effect on the total energy consumption. Based on characterization of energy consumption and moisture content of air source heat pump drying, the energy consumption was predicted using MLRM model and BPNN model. The corrective coefficients of determination of the MLRM model and BPNN model for energy consumption and end moisture content were 0.739 and 0.931, respectively. BPNN model with activation function of Sigmoid had the highest coefficient of determination of energy consumption, and the coefficient was 0.828. BPNN model with activation function of Identity had the highest coefficient of determination of end moisture content, and the coefficient was 0.942. The fitting effect was good and met the actual needs of production. Taking rehydrated peas as the drying object, a complete variable temperature and humidity drying process with a mass of 65 kg and a duration of 4 h was designed for verification test. The verification test results showed that the tested total energy consumption of the experiment was 15.066 kW·h, and the total energy consumption values predicted by MLRM model and BPNN model were 14.476 kW·h and 15.183 kW·h, with prediction accuracy of 96.08% and 99.23%, respectively. The tested end moisture content was 8.541%, and predicted moisture content by MLRM model and BPNN model were 9.560% and 8.889%, with prediction accuracies of 88.07% and 95.93%, respectively. This study analyzed the energy consumption characteristics of air source heat pump drying, and proposed an effective means for the prediction of energy consumption for air source heat pump drying, and the prediction was experimentally verified with high reliability of accuracy, which was of great practical significance for the drying process optimization and energy consumption reduction.