Heat Transfer Prediction of Supercritical Carbon Dioxide in Vertical Tube Based on Artificial Neural Networks

Accurate prediction of supercritical CO_(2)(scCO_(2))heat transfer is important for heat exchanger design and safe operation of scCO_(2)power cycles.The main prediction method is empirical correlation.This paper demonstrates an alternative way by artificial neural networks(ANN)model with two hidden layers.To assess widely cited correlations and newly developed ANN model,scCO_(2)heat transfer experiment in vertical tube with pressure up to 20.8 MPa was performed to extend experiment database,which includes 2674 runs.Compared with empirical correlations,the ANN model is promising for following advantages:(1)ANN model has much better prediction accuracy.The mean relative error,mean absolute relative error and the root-mean-square relative error between predicted and measured wall temperatures are eA=0.38%,eR=4.88%and eS=7.29%,respectively.(2)ANN model performs faster computation speed.(3)ANN model can accurately and speedily predict scCO_(2)heat transfer performance for both normal heat transfer and heat transfer deterioration modes.The trained ANN program is provided with this paper,which is a useful tool and can be directly applied in engineering of scCO_(2)heat transfer.

Energy-aware fuzzy job-shop scheduling for engine remanufacturing at the multi-machine level

The rise of the engine remanufacturing industry has resulted in increased possibilities of energy conservation during the remanufacturing process,and scheduling could exert significant effects on the energy performance of manufacturing systems.However,only a few studies have specifically addressed energy-efficient scheduling for remanufacturing.Considering the uncertain processing time and routes and the operation characteristics of remanufacturing,we used the crankshaft as an illustrative case and built a fuzzy job-shop scheduling model to minimize the energy consumption during remanufacturing.An improved adaptive genetic algorithm was developed by using the hormone modulation mechanism to deal with the scheduling problem that simultaneously involves parallel machines,batch machines,and uncertain processing routes and time.The algorithm demonstrated superior performance in terms of optimal value,run time,and convergent generation in comparison with other algorithms.Computational results indicated that the optimal scheduling scheme is expected to generate 1.7 kW∙h of energy saving for the investigated problem size.In addition,the scheme could improve the energy efficiency of the crankshaft remanufacturing process by approximately 5%.This study provides a basis for production managers to improve the sustainability of remanufacturing through energy-aware scheduling.