• Energy Research

Reactive dyeing is primarily used in the textile industry to achieve a high level of productivity for high-quality products. This method requires heating a large amount of freshwater for dyeing and cooling for the biological treatment of discharged wastewater. If the heat of the wastewater discharged from the textile industry is recovered, energy used for heating freshwater and cooling wastewater can be significantly reduced. However, the energy efficiency of this industry remains low, owing to the limited use of waste heat. Hence, this study suggested a cost-optimal heat exchanger network (HEN) in a heat pump-assisted textile industry wastewater heat recovery system with maximizing energy efficiency simultaneously. A novel two-step approach was suggested to develop the optimal HEN in heat pump-assisted textile industry wastewater heat recovery system. In the first step, the system was designed to integrate the heat exchanger and heat pump to recover waste heat effectively. In the second step, the HEN in the newly developed system was retrofitted using super-targeted pinch analysis to minimize cost and maximize energy efficiency simultaneously. As a result, the proposed wastewater heat recovery system reduced the total annualized cost by up to 43.07% as compared to the conventional textile industry lacking a wastewater heat recovery system. These findings may facilitate economic and environmental improvements in the textile industry.

Abstract The production and application of hydrogen, an environmentally friendly energy source, have been attracting increasing interest of late. Although steam methane reforming (SMR) method is used to produce hydrogen, it is difficult to build a high-fidelity model because the existing equation-oriented theoretical model cannot be used to clearly understand the heat-transfer phenomenon of a complicated reforming reactor. Herein, we developed an artificial neural network (ANN)-based data-driven model using 485,710 actual operation datasets for optimizing the SMR process. Data preprocessing, including outlier removal and noise filtering, was performed to improve the data quality. A model with high accuracy (average R2 = 0.9987) was developed, which can predict six variables, through hyperparameter tuning of a neural network model, as follows: syngas flow rate; CO, CO2, CH4, and H2 compositions; and steam temperature. During optimization, the search spaces for nine operating variables, namely the natural gas flow rate for the feed and fuel, hydrogen flow rate for desulfurization, water flow rate and temperature, air flow rate, SMR inlet temperature and pressure, and low-temperature shift (LTS) inlet temperature, were defined and applied to the developed model for predicting the thermal efficiencies for 387,420,489 cases. Subsequently, five constraints were established to consider the feasibility of the process, and the decision variables with the highest process thermal efficiency were determined. The process operating conditions showed a thermal efficiency of 85.6%.

AbstractMost factories employ an amine gas sweetening process to remove sulfur compounds. During this process, heat‐stable amine salt (HSAS) is generated, which reduces process efficiency. Ion exchange resin and electrodialysis reclamation methods are employed for HSAS removal. However, the equipment cost, electricity, water, and raw material consumption of each unit vary according to the amount of HSAS reclaimed; thus, it is necessary to consider the cost. In this study, we compared the techno‐economic aspects of the amine regeneration process for individual HSAS reclaiming units, developed process models to predict operation costs, and verified our simulation results by comparing actual operation and design data. The proposed mathematical models can be employed to calculate various costs incurred during amine regeneration with HSAS reclaiming units. The economic crossover point is derived at 36.6111 kg‐mol/d by utilizing the models. This enables economically feasible units to be identified based on the amount of HSAS reclaimed.