Low temperature exhaust gases carrying large amount of waste heat are released by steel-making process and many other industries, Organic Rankine Cycles （ORCs） are proven to be the most promising technology to re- cover the low-temperature waste heat, thereby to get more financial benefits for these industries. The exergy analysis of ORC units driven by low-temperature exhaust gas waste heat and charged with dry and isentropic fluid was per- formed, and an intuitive approach with simple impressions was developed to calculate the performances of the ORC unit. Parameter optimization was conducted with turbine inlet temperature simplified as the variable and exergy effi- ciency or power output as the objective function by means of Penalty Function and Golden Section Searching algo- rithm based on the formulation of the optimization problem. The power generated by the optimized ORC unit can be nearly as twice as that generated by a non-optimized ORC unit. In addition, cycle parametric analysis was performed to examine the effects of thermodynamic parameters on the cycle performances such as thermal efficiency and exergy efficiency. It is proven that performance of ORC unit is mainly affected by the thermodynamic property of working fluid, the waste heat temperature, the pinch point temperature of the evaporator, the specific heat capacity of the heat carrier and the turbine inlet temperature under a given environment temperature.
According to the features of melting process of regenerative aluminum melting furnaces, a three-dimensional mathematical model with user-developed melting model, burner reversing and burning capacity model was established. The numerical simulation of melting process of a regenerative aluminum melting furnace was presented using hybrid programming method of FLUENT UDF and FLUENT scheme based on the heat balance test. Burner effects on melting process of aluminum melting furnaces were investigated by taking optimization regulations into account. The change rules of melting time on influence factors are achieved. Melting time decreases with swirl number, vertical angle of burner, air preheated temperature or natural gas flow; melting time firstly decreases with horizontal angle between burners or air-fuel ratio, then increases; melting time increases with the height of burner.
An optimal method for prediction and adjustment on byproduct gasholder level and self-provided power plant gas supply was proposed.This work raises the HP-ENN-LSSVM model based on the Hodrick-Prescott filter,Elman neural network and least squares support vector machines.Then,according to the prediction,the optimal adjustment process came up by a novel reasoning method to sustain the gasholder within safety zone and the self-provided power plant boilers in economic operation,and prevent unfavorable byproduct gas emission and equipment trip as well.The experiments using the practical production data show that the proposed method achieves high accurate predictions and the optimal byproduct gas distribution,which provides a remarkable guidance for reasonable scheduling of byproduct gas.
