Complex computer codes are frequently used in engineering to generate outputs based on inputs, which can make it difficult for designers to understand the relationship between inputs and outputs and to determine the best input values. One solution to this issue is to use design of experiments (DOE) in combination with surrogate models. However, there is a lack of guidance on how to select the appropriate model for a given data set. This study compares two surrogate modelling techniques, polynomial regression (PR) and kriging-based models, and analyses critical issues in design optimisation, such as DOE selection, design sensitivity, and model adequacy. The study concludes that PR is more efficient for model generation, while kriging-based models are better for assessing max-min search results due to their ability to predict a broader range of objective values. The number and location of design points can affect the performance of the model, and the error of kriging-based models is lower than that of PR. Furthermore, design sensitivity information is important for improving surrogate model efficiency, and PR is better suited to determining the design variable with the greatest impact on response. The findings of this study will be valuable to engineering simulation practitioners and researchers by providing insight into the selection of appropriate surrogate models. All in all, the study demonstrates surrogate modelling techniques can be used to solve complex engineering problems effectively.
Exhaust gases from the smelting furnace have high temperature and mass flow rate, and there is huge potential to use them for energy-related purposes such as electricity generation, cooling and heating. Utilization of the gases for energy-related purposes would lead to fuel savings and emissions reduction. To use this potential, it is necessary to design proper systems and cycles and apply a heat recovery unit. Several technologies are useable for heat recovery depending on the characteristics of exhaust gases, such as their mass flow rate, temperature and compositions. Due to the higher potential of combined heating, cooling and power (CCHP) generation systems compared with the systems with a single output, a CCHP is designed and investigated in the present study by consideration of the specifications of the exhaust gases. The applied system in this study comprises a Supercritical CO2 (SCO2) cycle, heat exchanger and single-stage absorption chiller for simultaneous heating, cooling and power production. Engineering Equation Solver (EES) is employed to model the proposed system by considering the properties of the flows and characteristics of the components. To get deep insight into the effective parameters on the outputs of the designed system, the impact of three factors, namely the mass flow rate of the gases, the effectiveness of heat exchanger and temperature of exhaust gases, are analyzed and investigated by the implementation of sensitivity analysis. As one of the main conclusions, it is found that an increment in the mass flow rate of exhaust gases from 30 kg/s to 70 kg/s causes augmentation in the power generation from 2037 kW to 4754 kW. Furthermore, exergy analysis is carried out, and it is found that an increase in the temperature or mass flow rate of exhaust gases or a decrease in the effectiveness of heat exchangers would lead to decrement in the exergy efficiency of the system. According to the performed sensitivity analysis, the mass flow rate of exhaust gases has the most remarkable influence on the heating and cycle-generated power among the considered factors.