Predictive performance of a wastewater source heat pump using artificial neural networks

Abstract

A pilot-scale wastewater source heat pump was operated for 30 days to recover heat from waste bathwater and to warm up fresh bathwater. The results indicated that the fresh water successfully warmed up to the designated 45℃, 50℃, and 55℃ with the coefficients of performance of 2.3–3.5. Artificial neural networks including back propagation, radial basis function, and nonlinear autoregressive model with exogenous input were used to simulate this process. The root-mean-square error and coefficient of variation of the simulated results, using the experimental data taken on the first 18, 21, 24, and 27 days, respectively, as a package of training data, showed that taking the data measured on more days as the training data improved simulation accuracy. The nonlinear autoregressive model with exogenous input needed at least 24 days’ training data to achieve acceptable simulation results, the back propagation needed 27 days, while the radial basis function did not achieve acceptable results. Predictions based on the nonlinear autoregressive model with exogenous input modeling showed that the performance of the wastewater source heat pump system could gradually be stabilized within 42 days.

Practical application: This study showed that the wastewater source heat pump can recover heat from waste bathwater to warm up fresh bathwater, and also demonstrated that the artificial neural network, especially nonlinear autoregressive model with exogenous input, is appropriate for predicting heat pump performance. Using this system can reduce building energy consumption, while using the artificial neural network model can help operate and maintain the wastewater source heat pump system.