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The black liquor is valuable biomass that can be recycled in the form of CO 2 -neutral fuels and is burned in recovery boiler furnaces to produce steam for power generation. However, the combustion of biomass results in a large amount of ash deposits, which reduce the heat transfer efficiency. Therefore, all recovery boilers have an ash deposit removal system (ADRS). However, its operation is inefficient, significant amounts of steam energy are wasted, and CO 2 emissions are increased. To solve these problems, this study addressed optimizing ADRS to maximize biomass recycling as renewable energy for CO 2 reduction. This study mainly consists of two parts. First, to predict power generation, a process model of the recovery boiler with an ADRS was developed, and the model was used to define mathematical equations. Second, a mathematical model was proposed to maximize biomass recycling. The increase in biomass recycling is calculated by multiplying net power generation and the amount of saved biomass. As a result, 16.135 ton/d of black liquor was saved, and the CO 2 emissions were decreased by 541–996 ton/y following an increase in the power generation by 2,987 MWh/y and in the net present value by $ 16,797,303. [Display omitted] [ABSTRACT FROM AUTHOR]

Most 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. [ABSTRACT FROM AUTHOR]

• Optimization framework was developed to determine the optimal MADSCN configuration that satisfies the total biodiesel demand. • The variation of CO 2 emission and biodiesel demand was considered as the uncertain values. • Case studies examine the effect of CO 2 emission and diesel demand. To determine the optimal configuration of supply chain network for microalga-based biodiesel (MADSCN) considering uncertain values which influence the optimal strategies of microalga-based biodiesel production, we develop a two-stage stochastic model that considers the CO 2 supply and biodiesel demand as uncertain parameters, which simultaneously influence the potential locations for the construction of carbon-capture-and-storage systems to supply CO 2 as feedstock, and of biorefineries to produce and distribute the biodiesel as to regions in which it is in demand. The proposed framework is formulated using mixed-integer linear programming to identify the optimal MADSCN configuration that minimizes the overall cost of microalga-based biodiesel production. Variation of both uncertain factors in the stochastic model affect the configurations and operating strategies of MADSCN compared to the deterministic model that fixes the factors. The optimal superstructure of MADSCN was affected more significantly by uncertain biodiesel demand than by uncertain CO 2 emission. [ABSTRACT FROM AUTHOR]

This work suggests an optimal strategy to sort and recycle plastic waste as a renewable energy resource with maximizing economic feasibility and mitigating environmental pollution. To derive the optimal sorting and recycling strategies of plastic waste, a novel optimization model is developed; it calculates the overall profit by subtracting the profit of recycling plastic from the total annualized cost. Then the model is used to identify the optimal strategy to sort and recycle plastic waste as a renewable energy resource in mixed-integer nonlinear programming that maximizes the overall profit. In the derived optimal sorting and recycling strategy, high-density polyethylene is recycled to produce downgrade plastic; low-density polyethylene, polypropylene, and polystyrene are recycled as pyrolysis oil; and polyethylene terephthalate is recycled to produce refuse plastic fuel. The derived optimal case can significantly increase the overall profit by about 3,137% (i.e., 35 US$/1 kg of recycled plastic), and 492% (i.e., 29 US$/1 kg of recycled plastic) compared to conventional case in South Korea and Japan respectively. [Display omitted] [ABSTRACT FROM AUTHOR]

• Integration of the CO 2 injection approach as an enhanced gas recovery technique into an existing SGSCN model. • Development of an optimization framework to determine the optimal SGSCN configuration that improves gas productivity and reduces air pollution. • Case studies to examine the effect of CO 2 pulse injection on enhanced gas recovery. To optimize the configuration of a supply chain network for shale gas production (SGSCN), we develop a novel optimization model that considers 'enhanced gas recovery by carbon dioxide (CO 2) injection' (EGR-CO 2) technology, which simultaneously achieves decrease in net CO 2 emissions. Then, the developed framework is used to identify the optimal SGSCN configuration in a mixed-integer linear programming problem that maximizes the overall profit of shale gas production. The optimal framework of the proposed SGSCN model is compared to the case (Case 1) when the improvement technology for the shale gas production rate like EGR-CO 2 is not used, to demonstrate its superiority over existing approaches. The simulation results that consider application on the Marcellus shale play indicate that the overall profit of SGSCN that uses EGR-CO 2 technology and purchases the CO 2 on the market (Case 2) achieves 2.56% higher profit than the SGSCN without an injection strategy (Case 1) and 10.00% higher profit than the SGSCN that uses CO 2 that is recovered from the flue gases generated during combustion of shale gas to produce electricity (Case 3). The profitability of Case 3 is reduced by the cost of constructing and operating a CO 2 -capture facility. For Case 3 to achieve the same profitability as Case 2, the CO 2 purchase must be more expensive than 5 US$ per MCF CO 2 (0.18 US$ per m3). [ABSTRACT FROM AUTHOR]

In this study, we suggested an optimal strategy to sort plastic waste to improve recycling efficiency considering economic feasibility. To derive the optimal sorting strategy, we developed a novel optimization model that considers the overall cost, which is sorting cost minus the revenue obtained by selling the recycling plastic from the sorting cost. Then we used the developed model to identify the optimal strategy to sort plastic waste in mixed-integer programming that minimizes the overall cost of plastic waste sorting systems. We also conducted a sensitivity analysis to analyze the extent to which the results obtained can change under different conditions. The optimization results, identify that the plastics in the optimal sorting strategy are of four types: LDPE, HDPE, PP, and PVC. This optimal sorting strategy increase the overall sorting efficiency slightly by 4 wt%, but considering the revenue obtained by selling the recycled plastic the strategy significantly decreased the overall sorting cost by 69.28 % compared to the conventional case. The developed model can determine the optimal strategy to sort plastic waste considering economic improvement. Therefore, the results allow increase in plastic recycling by minimizing the overall cost of the sorting system. [ABSTRACT FROM AUTHOR]

This study proposes a novel integration of seawater bittern and waste seashell recovery processes for CO 2 and SO x utilization. Furthermore, simultaneous optimization of waste seashells and Ca(OH) 2 separated from seawater bittern as an SO x absorbent was conducted to derive the blending ratio using a genetic algorithm (GA). The GA-based optimization process consists of four steps: (1) data generation using process model and data preprocessing, (2) DNN-based surrogate model development to predict the gypsum purity, CaCO 3 yield, and CO 2 emissions, (3) mathematical model development for TAC calculation, (4) GA-based optimization to derive the cost-optimal blending ratio and TAC. As a result, the derived cost-optimal blending ratios were scallop shells (0.03 %), cockle shells (0.03 %), clam shells (13.66 %), oyster shells (40.31 %), mussel shells (28.80 %), and Ca(OH) 2 (17.18 %), reducing the TAC by up to 10.88 % compared to conventional CO 2 and SO x capture processes. At the optimal blending ratio, the recycling efficiency of each seashell in the Republic of Korea was achieved from a minimum of 2.72 % to a maximum of 99.15 % annually. Therefore, this work makes a significant contribution to the chemical engineering field, providing an environmentally friendly, economical process and an efficient simultaneous optimization framework applied to the proposed model. • The research proposes a novel process integrating seawater bittern and waste seashells for CO 2 and SO x utilization. • A genetic algorithm is used to optimize the blending ratios of seashells and Ca(OH) 2. • The optimized process significantly reduces the total annualized cost by up to 10.88 %. • This approach provides a sustainable technique, utilizing waste resources effectively. [ABSTRACT FROM AUTHOR]

[Display omitted] • Wet flue gas desulfurization process was optimized using waste oyster shell. • Deep neural network–based surrogate model is used to cost optimization. • Seashells with low-grade limestone could fully replace high-grade limestone. • At an optimal blending ratio, total annualized cost could be reduced by US$788,469. • The results facilitate cost-effective and eco-friendly SO x capture and utilization. In wet flue gas desulfurization system, the resource depletion of high-grade limestone, used as conventional SO x absorbent, is becoming serious for SO x capture and utilization. This paper proposes optimal selection and blending ratio of waste seashells as an alternative to high-grade limestone depletion using a deep neural network (DNN)-based surrogate model. Cost optimization proceeds as follows: data generation, data preprocessing, development of DNN-based surrogate model, and derivation of cost optimal selection and blending ratio. First, a process model is developed to generate the datasets, which are gypsum purity according to selection and blending ratio of each seashell and limestone. In addition, a mathematical model is proposed to calculate the total annualized cost (TAC) considering the pretreatment cost of seashell, and the TAC is added to the datasets to predict the gypsum purity as well as TAC. Second, the generated datasets are preprocessed to intensify prediction performance of the DNN-based surrogate model using the z-score normalization method. Third, a DNN-based surrogate model is developed to predict the gypsum purity and TAC according to the selection and blending ratio. Finally, the cost optimal selection and blending ratio are derived from 2.4 billion data generated by the developed DNN-based surrogate model under two constraints: gypsum purity and total SO x absorbent consumption. As a result, the derived selection and optimal blending ratios are low-grade limestone (80.86%), oyster shells (10.78%), scallop shells (0.216%), cockle shells (0.323%), clam shells (2.426%), and mussel shells (5.391%), reducing the TAC by US$788,469. [ABSTRACT FROM AUTHOR]

Wet flue gas desulfurization, which is performed in many thermal power plants, has a high desulfurization efficiency and produces desulfurized gypsum as a by-product. Currently, high-grade limestone with a CaCO 3 content of 94 wt% or more is used to produce desulfurized gypsum with a purity of 93 wt% or more. However, high-grade limestone resources are depleting, so new substitutes to this are required. The objective of this work is to optimize the wet flue gas desulfurization system using recycled waste oyster shells as high-grade limestone substitutes. The process model was developed for predicting the optimal blending ratio of waste oyster shells to limestone with constraints of desulfurization efficiency and purity of desulfurized gypsum. A mathematical model was addressed for optimized process costs, which was a nonlinear programming problem that minimizes the total annualized cost (TAC) by considering the total product cost (TPC) and equivalent annual cost; these vary according to the blending ratio. The optimal blending ratio of waste oyster shells is 16.160 wt% or 599.536 kg/h, TPC and TAC are reduced by $ 840,721 and $ 760,543, respectively. The waste oyster shell can be utilized 4,901 t/y, which is about 20.66 wt% of the total landfilled waste oyster shell. The results of this study suggest waste oyster shells, which pose landfill and odor problems, can be used as excellent substitutes for high-grade limestone for high economic and environmental benefits. [Display omitted] • Wet flue gas desulfurization process was optimized using waste oyster shell. • Optimal blending ratio of waste oyster shells is 16.160 wt% or 599.536 kg/h. • The oyster shell can be utilized 4901 t/y, which is by 20.7 wt% of total landfill. [ABSTRACT FROM AUTHOR]