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Although climate change can be efficiently curbed by shifting to low-carbon (blue) hydrogen as an energy carrier to achieve carbon neutrality, current hydrogen production mainly proceeds via the gray pathway, i.e., generates large amounts of CO 2 as a byproduct. To address the need for cleaner hydrogen production, we herein propose novel CO 2 capture processes based on the integration of vacuum pressure swing adsorption into a gray hydrogen production process and perform retrofitting to a blue hydrogen production process for on-site hydrogen refueling stations. Techno-economic analysis reveals that the implementation of the proposed capture processes allows one to significantly reduce CO 2 emission while preserving thermal efficiency, and the economic feasibility of this implementation in different scenarios is determined by computing the levelized cost of hydrogen. As a result, blue hydrogen is shown to hold great promise for the realization of sustainable energy usage and the net-zero transition. [Display omitted] • Techno-economic analysis of blue vs. gray on-site H 2 production was performed. • Gray H 2 process was coupled with CO 2 capture via vacuum pressure swing adsorption. • CO 2 emission could be reduced while preserving process thermal efficiency. • Levelized cost of H 2 was compared as economic feasibility for different scenarios. [ABSTRACT FROM AUTHOR]

The uneven distribution of particle flow, i.e., a different particle mass flow rate in each outlet of the riser in residue fluidized catalytic cracking (RFCC) processes, is one major problem associated with commercial RFCC processes. This problem affects the formation of carbonaceous deposits in the secondary reactor cyclone, which incurs serious catalyst carryover in the fractionators. This study analyzes particle-fluid flow patterns in the riser, and diagnoses the uneven distribution of particle flow using a computational particle fluid dynamics (CPFD) method to solve this real industrial problem. Through this analysis, the effect of the number of feed injectors is investigated. The CPFD method, which has been developed to complement the Eulerian-Eulerian and Eulerian-Lagrangian methods, applies the Navier-Stokes equation for fluid phase and multi-phase-particle-in-cell (MP-PIC) models for particle phase. The particle flow distribution was found to vary by 15.5–18.7% at different outlets in the 1 injector case, which implies that the solid loading ratio in each cyclone is different, thereby affecting the separation efficiency of the cyclone and the formation of carbonaceous deposits. The uneven distribution of particle flow phenomena was identified, and the standard deviations of particle mass flow rates were evaluated for the cases of 1, 2, 4, 6, 8 and 12 injectors, and were found to be 7.52, 4.07, 2.66, 1.78, 2.85 and 3.82, respectively. From these results, the 6 injectors case was found to have a largely even particle flow distribution. [ABSTRACT FROM AUTHOR]

Black liquor from pulp mills is valuable biomass that can be recycled as a CO 2 -neutral, renewable fuel. However, biomass combustion produces significant ash deposits reducing the overall process efficiency. A recovery boiler generally uses an ash deposit removal system (ADRS), but ADRS operation is inefficient, and the recycling efficiency of the biomass is decreased, leading to an increase in CO 2 emission. This work proposed an optimal operation of ADRS to improve the recycling efficiency of biomass for CO 2 emission reduction based on reinforcement learning. The optimal operation of the ADRS was derived by the following steps. 1) Real-time process operating data (i.e., temperatures of the flue gas, water, and steam) were gathered and a computational fluid dynamics model was developed to predict the flue gas temperature in the superheater section. 2) The decrease in the heat transfer rate was calculated using the gathered data to define a reward update matrix. 3) A modified Q-learning algorithm was developed based on the defined reward update matrix, and the algorithm was used to derive the Q-matrix, a function that predicted the expected dynamic reward (i.e., priority for ash deposit removal) of performing a given action (i.e., sootblowing) at a given state (i.e., each sootblowing location). 4) Using the obtained Q-matrix, the optimal operating sequence was derived. As a result, 22.58 ton/d of black liquor was saved and the CO 2 emission decreased by 755–1390 ton/y with an increase in the net profit by $1,010,000. [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]

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]

Sulfur dioxide (SO 2) is a pollutant that is mainly emitted in thermal coal-fired power plants, and it causes air pollution, which is required to be removed since it adversely affects the atmosphere environment. This study presents a novel approach to remove SO 2 using carbonate melt by designing a flue gas desulfurization (FGD) process that uses a carbonate melt desulfurization (CMD) subsystem to remove >99.9 wt% of SO 2 and a carbonate melt regeneration (CMR) subsystem that recovers ~99 wt% of the melt. To reduce the operating costs of the proposed process, a heat-exchanger network was developed to use the waste heat that existed in the process. The levelized cost of the proposed FGD process using carbonate melt was determined as US$ 761/ton SO 2 removed and is therefore cost-competitive with other SO 2 removal technologies. Sensitivity analysis indicates that coal price is the main driver of levelized cost. [ABSTRACT FROM AUTHOR]

[Display omitted] This study develops an elemental sulfur recovery (ESR) process from sulfur dioxide (SO 2) as a hazardous material removed from flue gas emitted at thermal coal-fired power plants with a carbonate melt flue gas desulfurization (CMFGD) process. The carbonyl sulfide (COS) generated as a byproduct after removing SO 2 from flue gas using carbonate melt in the CMFDG is utilized as a resource to produce elemental sulfur by applying the hydrolysis and Claus processes in the ESR process. In addition, to increase energy independence in the integrated CMFGD-ESR process, heat integration was applied by introducing new heat exchanger networks that utilize the waste heat in the proposed process. The levelized cost of the integrated CMFGD-ESR process was determined to be US$ 811 per ton SO 2 removed; from this result, the proposed process to remove hazardous material from flue gas emitted at thermal coal-fired power plants is economically benign compared to conventional SO 2 removal processes (US$ 500 ~ US$ 1200 per ton SO 2 removed), which use limestone as the raw material. [ABSTRACT FROM AUTHOR]

An energy source transition is necessary to realize carbon neutrality, emphasizing the importance of a hydrogen economy. The transportation sector accounted for 27% of annual carbon emissions in 2019, highlighting the increasing importance of transitioning to hydrogen vehicles and establishing hydrogen refueling stations (HRSs). In particular, HRSs need to be prioritized for deploying hydrogen vehicles and developing hydrogen supply chains. Thus, research on HRS is important for achieving carbon neutrality in the transportation sector. In this study, we improved the efficiency and scaled up the capacity of an on-site HRS (based on steam methane reforming with a hydrogen production rate of 30 Nm3/h) in Seoul, Korea. This HRS was a prototype with low efficiency and capacity. Its efficiency was increased through thermodynamic analysis and heat exchanger network synthesis. Furthermore, the process was scaled up from 30 Nm3/h to 150 Nm3/h to meet future hydrogen demand. The results of exergy analysis indicated that the exergy destruction in the reforming reactor and heat exchanger accounted for 58.1% and 19.8%, respectively, of the total exergy destruction. Thus, the process was improved by modifying the heat exchanger network to reduce the exergy losses in these units. Consequently, the thermal and exergy efficiencies were increased from 75.7% to 78.6% and from 68.1% to 70.4%, respectively. The improved process was constructed and operated to demonstrate its performance. The operational and simulation data were similar, within the acceptable error ranges. This study provides guidelines for the design and installation of low-carbon on-site HRSs. [Display omitted] • The capacity of hydrogen refueling station (HRS) was expanded from 30 to 150 Nm3/h. • Efficiency of on-site HRS was improved through exergy analysis. • Simulation data was validated with real operation data of the implemented process. • The fuel consumption and CO 2 emission were reduced. • This study provides design and efficiency improvement guidelines for on-site HRSs. [ABSTRACT FROM AUTHOR]

[Display omitted] • Oxy-fuel combustion-based blue hydrogen production process. • Integrates fossil fuel-based hydrogen production and electrolysis processes. • Proposed processes are SMR + SOEC and SMR + PEMEC. • SMR + SOEC shows highest thermal efficiency (85.2%) and exergy efficiency (80.5%) • SMR + SOEC shows lowest levelized cost of hydrogen and life cycle GHG emissions. Blue hydrogen is gaining attention as an intermediate step toward achieving eco-friendly green hydrogen production. However, the general blue hydrogen production requires an energy-intensive process for carbon capture and storage, resulting in low process efficiency. Additionally, the hydrogen production processes, steam methane reforming (SMR) and electrolysis, emits waste heat and byproduct oxygen, respectively. To solve these problems, this study proposes an oxy-fuel combustion-based blue hydrogen production process that integrates fossil fuel-based hydrogen production and electrolysis processes. The proposed processes are SMR + SOEC and SMR + PEMEC, whereas SMR, solid oxide electrolysis cell (SOEC), and proton exchange membrane electrolysis cell (PEMEC) are also examined for comparison. In the proposed processes, the oxygen produced by the electrolyzer is utilized for oxy-fuel combustion in the SMR process, and the resulting flue gas containing CO 2 and H 2 O is condensed to easily separate CO 2. Additionally, the waste heat from the SMR process is recovered to heat the feed water for the electrolyzer, thereby maximizing the process efficiency. Techno-economic, sensitivity, and greenhouse gas (GHG) analyses were conducted to evaluate the efficiency and feasibility of the proposed processes. The results show that SMR + SOEC demonstrated the highest thermal efficiency (85.2%) and exergy efficiency (80.5%), exceeding the efficiency of the SMR process (78.4% and 70.4% for thermal and exergy efficiencies, respectively). Furthermore, the SMR + SOEC process showed the lowest levelized cost of hydrogen of 6.21 USD/kgH 2. Lastly, the SMR + SOEC demonstrated the lowest life cycle GHG emissions. In conclusion, the proposed SMR + SOEC process is expected to be a suitable technology for the transition from gray to green hydrogen. [ABSTRACT FROM AUTHOR]

In textile industries, a lot of wastewater are discharged which are one of the major environmental pollution problems, because they release undesirable dye effluents. Owing to re-dyeing procedures performed to meet customized color specifications, environmental pollution is a serious problem because of the emission of large volumes of wastewater. To solve the environmental problems caused by re-dyeing, the right-first-time (RFT) %, which is the rate at which the target quality is obtained with just one dyeing, must be increased by considering the dyeing conditions that affect product quality. Here, this study suggests a framework for cleaner production of textile dyeing process using novel exhaustion-rate meter (NERM) and multi-layer perceptron-based prediction model to solve the environmental problems caused by re-dyeing procedure by controlling the exhaustion-rate outliers. The proposed NERM measures the exhaustion-rate based on absorbance of the dyeing solution and is composed of measuring and analysis section. The dyeing solution absorbance is metered in the measuring component through a detector, which performs high-resolution measurement (0.3–1.5 nm full width at half maximum) via a 25-μm slit in the 200–1100-nm wavelength range; the absorbance is then converted to the exhaustion-rate based on Beer's law in the analysis section. Using the NERM, an exhaustion rate dataset according to the Na 2 SO 4 and Na 2 CO 3 consumption is acquired and a surrogate model that augments the exhaustion rate data is developed. The MLP-based prediction model is then developed using the augmented data to control the real-time exhaustion-rate outliers. As a results, the model performance as regards Na 2 SO 4 and Na 2 CO 3 prediction is indicated by R 2 values of approximately 0.985 and 0.998, respectively, and root mean squared errors (RMSE) of approximately 1.477 and 1.000, respectively. In addition, the effectiveness of the proposed framework is demonstrated through application to several scenarios in which the real-time exhaustion rate outliers are detected. [ABSTRACT FROM AUTHOR]

The variance among bin counts is one of the most effective and convenient indices to quantify the degree of spatial distributive mixing. Although it is suitable for evaluating the spatial distribution of unimodal particles, many practical particle-mixing processes involve bimodal or multimodal particle systems. Herein, the variance among bimodal bin counts is introduced as a new mixing index to quantify the degree of distributive mixing of bimodal or multimodal particles. Four bimodal particle-mixing systems are assumed and analyzed to evaluate index performance: balanced versus imbalanced and fully versus partially distributed particle systems. As a result, we suggest practical usage and the most effective variation of variances among conventional bin counts and bimodal bin counts to quantify the four bimodal particle-mixing systems. Furthermore, variations of the method for evaluating multimodal mixing are proposed. [ABSTRACT FROM AUTHOR]

• Novel waste energy recovery process for carbon capture and storage was developed. • Waste cold and hot energy from liquefied natural gas plant is recovered. • Net power generation can increased by 16% compared to base process. • Net profit can increased by 75% compared to base process. In an liquefied natural gas (LNG) power plant, the amine scrubbing and CO 2 liquefaction process are generally employed for CO 2 capture and storage (CCS) because it is suitable for large-scale plants. However, a large amount of hot and cold energy is required for CO 2 absorption, regeneration and liquefaction. As a solution, the waste LNG cold energy from the LNG regasification process and waste hot energy from natural gas combined cycle (NGCC), which are generally disposed into the seawater, can be recovered and utilized for the abovementioned purposes. Hence, this study suggested a novel process for waste hot and cold energy recovery of the LNG power plants for CCS. The suggested process model consists of the following four steps: LNG regasification, natural gas combined cycle, CO 2 capture and regeneration and CO 2 liquefaction. In the process model, the waste LNG cold energy is recovered at the lean amine cooler in the CO 2 capture process and each heat exchanger in the CO 2 liquefaction process. Furthermore, for CO 2 regeneration, the waste hot energy from NGCC is recovered at the stripper reboiler. The exergy and economic analyses were addressed to evaluate the economic feasibility of energy conversion of the proposed process. As a result, compared to the base process, the net power generation and exergy efficiency of the proposed process increased by 16% and 8%, respectively. In addition, the net profit of the proposed process increased by 75%, indicating high economic feasibility. The overall energy efficiency and economic feasibility using waste cold and hot energy were observed to increase, which resulted in decreased fuel usage. Therefore, we believe that the proposed approach can contribute significantly to the economic improvements and environmental protection efforts. [ABSTRACT FROM AUTHOR]

Thermosetting polymers are used in a wide range of applications due to their robust mechanical strength and superior flame retardancy. Despite these technical benefits, recycling of thermosetting polymers has been challenging because of their crosslinking nature. Moreover, their disposal through conventional methods (landfill and combustion) poses environmental concerns, such as microplastics and air pollutants. To address these issues, this study introduces a thermo-chemical disposal platform for thermosetting polymer wastes that employs carbon dioxide (CO 2) as a reactive medium. In this work, melamine-formaldehyde was used as model compound of thermosetting polymers. In single-stage pyrolysis, it was revealed that CO 2 plays a crucial role in controlling in the compositional matrices of pyrolytic gases, liquid products, and wax. These compositional changes were attributed to the homogeneous reactions between CO 2 and the volatile compounds released from the thermolysis of MF. To enhance the thermal cracking of the MF, a double-stage pyrolysis process was tested, which increased the production of pyrolytic gases and eliminated wax formation. However, the slow kinetics governing the reactivity of CO 2 limits the occurrence of homogeneous reactions. A nickel-based catalyst was used to accelerate reaction kinetics. The catalytic pyrolysis under CO 2 conditions led to substantial increases in syngas (H 2 and CO) production of 880% and 460%, respectively, compared with double-stage pyrolysis. These findings demonstrate that thermosetting polymer wastes can be valorized into gaseous fuels through thermo-chemical process, and CO 2 enhances the recovery of energy and chemicals. Therefore, this study presents an innovative technical platform to convert thermosetting polymer wastes and CO 2 into syngas. [Display omitted] • A sustainable pyrolysis framework for disposal of MF waste was suggested. • CO 2 plays a crucial role in controlling composition of the pyrogenic products of MF. • MF was converted to value-added products through pyrolysis. • Ni-based catalyst resulted in CO enhancements 5 times higher under N 2 condition. [ABSTRACT FROM AUTHOR]

[Display omitted] • On-site hydrogen refueling stations require efficient catalysts/reactors. • Intrinsic kinetics of Ru catalysts were estimated using a parametric estimation. • New multistage (two- and three-stage) reactor designs were proposed. • Multistage reactors showed less temperature drop (better reactor stability). • Three-stage reactor can improve hydrogen production and conversion. In recent years, interest in the development of a hydrogen economy has increased, and the steam methane reforming (SMR) reaction, which yields so-called "gray" hydrogen, is expected to play a key role in hydrogen refueling stations (HRSs) in the interim before "green" hydrogen process is developed. For this purpose, compact and economical designs for small- to medium-scale SMR processes for HRSs are required. Herein, we investigated the intrinsic kinetics of a Ru-based catalyst using catalyst experiments and parametric estimation. In addition, reactor modelling was performed using a kinetic model with a special focus on multistage reactor configurations. The proposed reactor design showed an increase in the reactor energy supplied rate from 6.98 to 7.55 kW compared to the base case (single-stage reactor), and the conversion increased from 86.2% to 91.2%. Further, the drastic temperature drop owing to the strongly endothermic nature of the SMR reaction was reduced, and, thus, reactor stability was improved. The proposed novel design of a multistage reactor using a Ru-based catalyst will advance the development of the hydrogen economy. [ABSTRACT FROM AUTHOR]

Currently, hydrogen is produced primarily through steam methane reforming, a gray hydrogen production process that generates CO 2 as a by-product. Thus, it is crucial to optimize the process thermal efficiency with minimizing CO 2 generation in a hydrogen production process. This study focuses on the multi-objective optimization of low-carbon hydrogen production process, considering both process thermal efficiency maximization and CO 2 emission minimization. To this end, a hybrid deep neural network model is developed to increase the robustness of the multi-objective optimization. The developed hybrid deep neural network model is incorporated into a proposed multi-objective particle swarm optimization algorithm that performs Pareto dominance-based multi-objective optimization. In experiments conducted, Pareto-optimal solutions with thermal efficiency distribution between 77.5 and 87.0% and CO 2 emissions between 577.9 and 597.6 t/y were obtained. Furthermore, the Pareto-optimal front was analyzed to provide various representative solutions to assist decision-makers. The findings of this study can enable efficient and flexible process operations according to various requirements. [Display omitted] • Low-carbon hydrogen production from an on-site steam methane reforming process. • Multi-objective optimization of thermal efficiency and CO 2 emission simultaneously. • Multi-objective particle swarm optimization for Pareto dominance-based optimization. [ABSTRACT FROM AUTHOR]

Fluidized bed incinerators are more efficient and safe for treating explosive waste than previous methods because they can emit nitrogen oxide (NOx) concentrations below the standard value (90 ppm). However, a limitation is that they have only focused on optimizing the operating conditions to minimize NOx emission concentrations till now. In this situation, it is crucial to balance NOx and process costs. Therefore, this study designed an explosive waste incineration process and performed multi-objective optimization. An artificial neural network surrogate modeling method is vital to reduce optimization time. Therefore, surrogate models with 95% and 99% accuracies were obtained, reducing the calculation time by 90%. Furthermore, an index combining NOx emission concentrations and process costs was proposed to obtain an optimal balanced operating condition of the process. By optimizing the process index, a new operating condition was obtained that could reduce 20% of the process costs while maintaining NOx emission concentrations within the standard limit. The proposed operating condition and data, such as from sensitivity analysis, would provide a valuable guideline for operating the abovementioned process associated with NOx emission standards. [Display omitted] [ABSTRACT FROM AUTHOR]

[Display omitted] • LP-HTL and MSP increased gas yield & achieved high monomer recovery. • LP-HTL and MSP achieved ethylene and propylene yield of 53.07 %. • Energy requirements reduced by 95.82 % by eliminating refrigerants from the process. • GTP100 and GWP100 values were 90 % lower than those of the reference process. • The olefins, monomers of plastics, can be used to manufacture circular plastics. Plastics pose environmental challenges in landfills, persisting for extended periods spanning thousands to millions of years. Consequently, research into plastic depolymerization has gained significance, aiming to address the problem of plastic waste management and the increasing demand for plastics. This study proposes a novel process for recovering olefins, specifically ethylene and propylene, from waste polyethylene (PE) and polypropylene (PP) through low-pressure hydrothermal liquefaction (LP-HTL) and microwave steam pyrolysis (MSP) chemical recycling. Mixed waste PE and PP undergo LP-HTL to produce gas and oil. Subsequently, the oil from the LP-HTL undergoes cracking via MSP to enhance olefin recovery. Olefin compounds produced through distillation serve as refrigerants. The results demonstrated the production of 39.75 wt% C 2 H 4 and 13.32 wt% C 3 H 6 , achieving a total recovery of 53.07 wt% of olefin materials. The levelized cost of ethylene (LCOE) in the proposed process was calculated at 0.89 USD/kg C 2 H 4 , equating to a 72.86 % reduction compared with that in flash pyrolysis. Furthermore, the life cycle assessment (LCA) results indicated reduced 100-year global temperature potential and global warming potential (GTP100 and GWP100) emissions of 2.46 and 2.55 kg CO 2 eq/kg C 2 H 4 , respectively, approximately 90 % lower than that in the flash process. Thus, the proposed process, with its energy efficiency and high recovery rates, can serve as a benchmark for future plastic depolymerization endeavors aimed at achieving a circular carbon economy. [ABSTRACT FROM AUTHOR]

• DNN data-driven model for steam methane reforming is developed. • Data preprocessing techniques were applied to improve the quality of datasets. • Four hyperparameters were optimized to obtain an accurate prediction model. • Process optimization was conducted with 387,320,489 cases under five constraints. • The operating conditions were optimized to achieve a thermal efficiency of 85.6%. 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 R 2 = 0.9987) was developed, which can predict six variables, through hyperparameter tuning of a neural network model, as follows: syngas flow rate; CO, CO 2 , CH 4 , and H 2 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%. [ABSTRACT FROM AUTHOR]

Stenters used for drying during the dyeing process, waste considerable thermal energy through exhaust gas. This energy waste is one of the major bottlenecks to improving the sustainability of the textile dyeing field. To find the most efficient recovery of the waste heat from the stenters for supplying various energy sources and reducing energy consumption within overall textile system, this study compares three heat recovery systems: (Case- 1) air preheating, (Case-2) hot-water generation, and (Case-3) power generation. Energy, exergy, and economic analyses indicated that all cases demonstrated superior results those obtained using the base case. In terms of energy consumption, Case-1 demonstrated a 60.8 % reduction achieved through decreased fuel consumption. In particular, the economic analysis indicated that Case-1 was the most favorable option because it possesses the lowest operating costs by significantly reducing fuel consumption for air preheating. Additionally, the simple configuration of the process and the use of a small heat exchanger contribute to a lower capital cost. Thus, Case-1 could the most efficiently recover and utilize the heat wasted from the stenter at a relatively low investment cost among proposed systems. This demonstrated the potential for energy and cost savings within the textile industry. [Display omitted] • Heat recovery systems in the stenter reduce the use of electricity, steam and fuel. • Case 1 has low expense due to low coolant use, simple design and fuel reduction. • Low hot water demand in the dyeing process made case 2 less economical than case 1. • This study indicates the potential for greater energy savings in textile industry. [ABSTRACT FROM AUTHOR]

Steam methane reforming (SMR) is widely used in the hydrogen production industry; however, a significant amount of CO 2 is released during this process. Several efforts have been made to produce low-CO 2 hydrogen (blue hydrogen) via SMR; however, the proposed solutions are not applicable to small-scale plants. Therefore, this study proposes an on-site SMR process combined with a hollow fiber membrane module (HFMM) for CO 2 capture in small-scale plants. First, mathematical models for the on-site SMR process and HFMMs were developed, and their accuracy was validated with real-world data. Second, we designed and implemented the SMR–HFMM model based on different operating conditions and gas compositions at three potential CO 2 capture locations (dry syngas, PSA tail gas, and flue gas). The CO 2 capture performances at these three locations were compared using five performance indicators: stage cut, separation factor, CO 2 recovery rate, permeate composition, and retentate composition. Finally, to evaluate the integrated processes for each CO 2 capture location, feasible ranges of the number of HFMMs and the levelized cost of hydrogen (LCOH) were calculated. In the case of CO 2 captured in dry syngas, the number of HFMMs required to achieve a CO 2 purity of over 90% was calculated to be 10–25. Furthermore, despite additional HFMM installation, the LCOH was 0.8%–1.5% lower than that of the conventional on-site SMR process that is 7.07–7.13 USD/kgH 2. The proposed integrated SMR–HFMM process is a potential solution to the problem of CO 2 emissions in on-site SMR processes with a lower LCOH. Therefore, the findings of this study could be of significant importance in improving the environmental sustainability of hydrogen production in small-scale plants. [Display omitted] • Steam methane reforming (SMR) emits CO 2. • Conventional blue hydrogen production methods are not appropriate for on-site SMR plant. • Integration of on-site SMR with a hollow fiber membrane module (HFMM) for hydrogen production was proposed. • HFMM could capture CO 2 with high efficiency in a real on-site SMR plant. [ABSTRACT FROM AUTHOR]

The concept of heat integration with cryogenic energy storage (CES) is a possible option for the recovery of wasted cold energy from liquefied natural gas (LNG). For maximizing energy storage capacity, we propose a conceptual design for a massive cryogenic energy storage system integrated with the LNG regasification process (MCES). The novel aspect of this study is the transmission of LNG cold energy via two different methods at different times: (1) MCES stores cold energy in liquid propane during on-peak times, enabling increase in the energy storage capacity; and (2) MCES directly transfers cold energy with help of liquid propane during off-peak times to liquefy air using surplus electricity from the grid. Thus, the surplus energy is stored in liquefied air and released to generate electricity on demand. Based on the process simulation, exergy analysis and economic evaluations are conducted. MCES exhibits a round trip efficiency of 85.1%, whereas existing bulk power management systems exhibit a maximum efficiency of 75%. Moreover, using a three-million-ton-per-annum LNG regasification plant, MCES enables the supply of 138 MW of electrical power which is up to 96% more power than that achieved by other recently proposed process designs, and has potential for bulk power management. • Massive cryogenic energy storage system utilizing LNG cold energy is developed. • MCES provides 96% improved energy storage capacity than recent systems. • MCES exploits LNG cold energy in two different ways according to time variances. • MCES is economically feasible, having a round trip efficiency of 85.1%. • MCES allows 11.7% substitution of non-baseload power generation of South Korea. [ABSTRACT FROM AUTHOR]

Among the various physical properties of polypropylene composites (PPCs), heat deflection temperature (HDT) during PPC production is significant because it is directly related to the mechanical behavior of such products. However, it is difficult to predict and analyze the HDT of PPCs owing to the absence of a mathematical or theoretical model. Moreover, categorical data which has different physical properties despite using same substances made predictions highly difficult in PPCs. Here, this study proposed an indicator to analyze the categorical data and Catboost-based model for HDT prediction considering the categorical data. First, the categorization and minimum-based values (MBVs), a dimensionless factor used to calculate HDT differences in the categorical dataset, are applied to detect and split categorical data in a PPC dataset. Second, a case study was conducted on the dataset using three algorithms to compare the proposed model with other traditional machine-learning approaches. As a result, the proposed model provides the highest prediction performance of R 2 = 0. 8965 and 0.9801, for the total test dataset and the categorical dataset, respectively. In addition, the effect analysis of substances on HDT was conducted to get some prospective substances for the required HDT using Shapley Additive Explanations (SHAP). Thus, the results of this study can provide a guidance for selecting prospective substances using SHAP result for the target HDT and adjusting the substance ratio using the proposed model. It is expected that this framework has the potential for being applied to the other blending processes to produce products with the required properties. • This study is the first attempt to develop a framework for predicting the HDT using machine learning-based approaches. In addition, an effect analysis on PPCs using SHAP is proposed to reduce the number of trial and error attempts during the manufacturing of PPCs. • This study proposes a machine learning-based HDT prediction model with a high prediction performance for both normal and categorical data. The best model was developed by comparing three machine learning algorithms after hyperparameter optimization. • The applicability of the proposed framework, the development of the machine learning-based HDT prediction model, and the results of SHAP are demonstrated by applying them to a commercial PPC dataset in the chemical industry. [ABSTRACT FROM AUTHOR]

Abstract The continuous mixing process of powder is used in many industries. Careful evaluation of the degree of the mixing is essential to enhance equipment performance. However, no single evaluation method has been universally adopted for the continuous mixing process of bimodal particles due the methods' poor applicability across multiple process concepts. Presented here are two indices to evaluate mixing in the transverse and axial directions. In this study, the indices are estimated at steady state based on Variance among Bimodal Bin Counts (VBBC), which was introduced as an evaluation method of a bimodal particle mixing system in our previous study. The VBBC mixing index for a continuous mixer is introduced to evaluate the transverse mixing of particles, while an axial stability factor is introduced to evaluate the mixing in the axial direction, which is indicative of the consistency of the process. The importance of each index depends on the characteristics of the process. Therefore, several strategies are suggested for applying the methods to practical cases. In addition, a hypothetical example is presented to illustrate their application: five continuous mixers having differently angled blades are assumed because the blade angle is the most easily modifiable design factor. The bimodal particle mixing processes are simulated using the Discrete Element Method (DEM). Based on the application example, both indices are calculated and the optimal design of a screw is suggested. Graphical abstract Unlabelled Image Highlights • Novel method is developed to evaluate the continuous powder mixing process. • Variance among Bimodal Bin Counts is adopted for the novel method. • The method includes two indices to evaluate the transverse and the axial mixing. • Several strategies are introduced to apply the method to practical systems. • Five continuous powder mixers are simulated with DEM and evaluated with the method. [ABSTRACT FROM AUTHOR]

Highlights • Regulations and guidelines for risk management of chemical plant in Korea is introduced. • Reducing damage to surrounding areas from a chemical-plant accident by building a mitigation barrier. • Height of barrier is 20–33 m and length is 350 m to reduce vapor cloud dispersion. • Computational fluid dynamics used to study barrier's mitigation effect. Abstract After the hydrogen–fluoride release accident in 2012, the Korean society realized the importance of chemical safety and many plans have been proposed to improve it. After the big chemical accident, the "Chemical Control Act" was newly established. The law of the "Chemical Control Act" is the most representative measure for chemical safety. According to the law that came into force in 2015, all chemical dealing companies must conduct an off-site consequence analysis of their chemicals and develop a plan for risk management. To reduce off-site consequences from the chemical plant, an innovative risk-management plan was suggested by the Korean industry. A decision was made to build a 30 m high mitigation barrier outside the plant area to protect the public when a chemical release accident occurs. The construction is now under process, and two representative accident scenarios are developed for its simulation to confirm the effect of mitigation barrier. Each scenario follows guidelines of the "Chemical Control Act," and simulation results show that the barrier helps reduce chemical concentration in the public area. This plan is expected to improve the anxiety of residents near the plant and will be a good example of risk management in the industry. [ABSTRACT FROM AUTHOR]

• The profit of the NCC was optimized considering uncertainties. • Industrial data were used to quantify the uncertainty by creating PDFs. • The PCE-based model was developed to predict uncertainties. • Pareto front solutions are proposed using genetic algorithm. • Guidelines of operating COT can be provided to the decision maker. A cracking furnace is the primary unit of a naphtha cracking center (NCC) to produce ethylene (EL) and propylene (PL); the yields of EL and PL depend on the naphtha composition of the NCC feedstock. However, the naphtha composition typically fluctuates depending on the feedstock suppliers, and the consequent uncertainties in the composition causes investment risks associated with the net profit. However, owing to the high dimensional uncertainties of the naphtha, conventional sampling (e.g. Monte Carlo method) based robust optimization is infeasible option due to high computational cost. In this study, we adopt the polynomial chaos expansion (PCE) to surrogate the system considering the uncertainty. Owing to the orthogonality of the PCE, the statistical moment of the PCE can be directly calculated without uncertainty sampling and iterative simulation. In this study, the PCE-based profit optimization model of the NCC furnace is developed as follows: Initially, we infer probability density functions (PDF) using industrial data to consider the uncertainty in the naphtha composition. Using the inferred PDF, we extract training dataset samples with coil outlet temperature (COT), product price, feed composition, and the net profit. Subsequently, using the training dataset, we develop a polynomial chaos expansion (PCE)-based surrogate model to predict the moments of the net profit, namely, the mean, variance, and skewness. Owing to the orthogonality of the model, the moments can be parameterized with only decision variables instead of computing the uncertainty. Finally, we incorporate the developed PCE-based model into a genetic algorithm to simultaneously optimize two conflicting objectives: maximizing the mean profit and minimizing the variance. The optimization results reveal the trade-off relationship between the mean profit and investment risk (variance and skewness) of the NCC process under feed uncertainty. Owing to the orthogonality, the optimal decision point can be provided with low computation time and high prediction accuracy compared with the sampling based optimization method. Considering the application of the proposed optimization model, we conduct case studies for two different scenarios of the product price. The optimal COTs for maximum mean profit with minimum variance of profit in the first and second scenarios range from 723 to 833 and 734 to 898 ℃, respectively. Therefore, the proposed model can quantitatively predict the mean profit with investment risk and help decision-makers select optimal operating conditions considering both the investment tendency and uncertainty in the naphtha composition. [ABSTRACT FROM AUTHOR]

Shadowgraphy-based image processing studied in the literature on multiphase processes has led to meaningful advances in mass transfer enhancement via interfacial area enlargement. However, the industrial applications of shadowgraphy have been limited due to the requirement of an additional light source at specific locations. To overcome this limitation, in this study, a new bubble size measurement technique in low-light conditions is proposed. The technique uses reflected LED image on the bubble surface to estimate the bubble size in low-light conditions and includes a newly derived measurement correlation model, which was validated with lab-scale experimental data. Furthermore, the proposed model was applied to industrial-scale bubble systems for hydrogen fluoride (HF) removal. Using the bubble properties identified through analysis, the overall mass transfer coefficient (OMTC) was determined as an indicator of HF mass transfer enhancement. The optimal conditions for HF mass transfer were determined by identifying the system with the highest OMTC. By manipulating the pressure difference and flow rate, OMTC was increased by ∼78% of the base case. [Display omitted] [ABSTRACT FROM AUTHOR]

[Display omitted] At public bus stops, NO X pollutants discharged by regularly stopping buses quickly accumulate, exposing waiting passengers to high levels of air pollutants, which creates a threat to public health. The environmental protection agency (EPA) presents air quality standards for NO X , a significant pollutant that causes lung diseases such as asthma when exposed to the human body. To handle this problem, air purification systems are installed inside bus stops in many public places. However, it is challenging to maintain a low concentration of NO X inside public bus stops due to the persistent inflow of bus exhaust gas. Therefore, it is crucial to design an optimal location for an air purification system to meet air environment standards for respiratory areas. This study proposed a computational fluid dynamics (CFD)-based optimal installation strategy for an air purification system to minimize NO X exposure inside a public bus stop. The CFD model was developed to numerically analyze NO 2 exposure with the actual design value for a public bus stop in Ulsan, South Korea. The local NO 2 concentration was evaluated in the human breathing zone. The case study was performed according to the locations of the inlet and outlet of the air purification system. A transient CFD simulation was performed to analyze the effect of the air purification system on pollutants generated from the stationary bus by time flow in various cases. NO 2 concentration and exposure reduction effectiveness (ERE) were analyzed and compared for each case in the breathing zone. In the optimal case, the ERE of NO 2 was confirmed to be 35.9 %, and the NO 2 concentration according to the air quality standards of EPA could be maintained at 0.1 ppm or less. The theoretical framework proposed in this study can be generalized to design air purification systems for general external facilities. [ABSTRACT FROM AUTHOR]

In the pulping process, the recovery boiler produces "green" steam without the use of fossil fuels as it burns the biomass (black liquor). However, because the black liquor concentration generated during the woodchip cooking is not suitable for combustion, it is concentrated using an evaporator that uses a large amount of steam energy for evaporation. The concentrated black liquor is sprayed to burn in a recovery boiler furnace, and combustion air is simultaneously supplied through an air distribution system. Biomass concentration and combustion air distribution should be optimized to realize sustainable cleaner production by maximizing biomass recycling and energy efficiency. In this study, the optimal operating conditions of the evaporator and combustion air distribution system in a pulp mill were determined simultaneously for biomass recycling and energy efficiency maximization. First, a process model was developed by integrating an evaporator and a recovery boiler furnace with an air distribution system. Using this model, the energy consumption of the evaporator, energy generation in the recovery boiler, and amount of recycled pulping chemicals were predicted for profit and cost estimation. Second, a mathematical model was developed to derive the optimal operating conditions. In this model, the net profit was calculated by subtracting the steam cost in the evaporation process from the profit of steam production and recovered pulping chemicals. Finally, the optimal biomass concentration and combustion air distribution were determined to maximize the net profit. As a result, the derived optimal operating conditions increased the annual power generation by 7,491 MWh/y and the amount of recovered pulping chemicals by 68,602 t/y. In addition, the net profit increased by 11.82%, and the annual CO 2 emissions decreased from 2,504 to 1,361 tons, making the pulping process more sustainable. The findings of this study promote cleaner production in the pulping process by substituting fossil fuels with biomass and maximizing energy efficiency. • Biomass concentration and combustion air distribution in pulp mill were optimized. • The evaporator and the recovery boiler furnace were integrated into a process model. • Novel mathematical model optimizes the operating conditions. • Biomass recycling and energy efficiency were maximized. • Economic and environmental benefits of the pulping process were achieved. [ABSTRACT FROM AUTHOR]

• The training data selection method using time-series clustering is proposed. • The proposed method is applied to commercial 2,3-BDO distillation process. • The number and ratio of training data are optimized by mathematical model. In this study, we propose a time-series clustering approach that selects optimal training data for the development of predictive models. The optimal number of clusters was set based on the variation of within-cluster sums of squares. A predictive model was developed with the selection ratio of training data from each of those clusters. Based on the results, a regression model was developed to predict the performance of the model. The search space was applied to the regression model, and the optimal training data ratio were selected satisfying the objective function and constraints. The effectiveness of the method is demonstrated by addressing a commercial bio 2,3-butanediol distillation process. As a result, the number of data for model training was reduced by 49.20% compared to the base case without clustering. The coefficient of determination (R2) showed the same level of performance, and the root-mean-square error was improved up to 14.07%. [ABSTRACT FROM AUTHOR]

• The CFD model was developed and validated with results of the experimental device. • We solved the low cooling performance of ACHEs and corrosion problem simultaneously. • Optimum amounts of water for cooling to the target temperature were presented. • The CFD model was applied to design the commercial process. Air-cooled heat exchangers (ACHEs) are one of the most efficient and widely used equipment for heat exchange. However, the main disadvantages of ACHEs are their heat exchange performance is decreased by high ambient air temperature (T a), especially on summer season. To solve this problem, a water mist system that sprays an amount of water is applied to ACHEs. However, the water mist system causes corrosion problems of the peripheral devices. Therefore, it is crucial to find a proper water mist system with optimized operating conditions that improve the cooling effect without corrosion. Herein, we developed the CFD model to find the optimal amount of water sprayed. Cooling effect and water evaporation ratio according to the amount of water sprayed and Ta were calculated using the CFD model. As a result of the calculation, the amount of water that gives the target cooling effect as all water evaporates is 3.364 kg/h and 7.928 kg/h at Ta of 303.15 K and 313.15 K, respectively. This study provides the optimal amount of water sprayed on the water mist system considering the cooling effect and corrosion according to Ta, and it can apply to the commercial processes. [Display omitted] [ABSTRACT FROM AUTHOR]

● Degree of mixing was calculated at the top of an adsorbent layer of simulated moving bed. ● Novel design diversified the direction of injected flow by adopting a circular flow distribution box. ● Regardless of the criteria applied, the degree of mixing was improved in the novel design. ● Simulation was validated using residence time distribution analysis and has a correlation value of 0.98 with experiment. Mixing between fluids is an important factor affecting the performance of simulated moving bed (SMB). During the SMB process, fluids periodically flows in and out, imperfect mixing of the fluids decreases the adsorption efficiency. In this study, the mixing effect of a conventional flow distribution apparatus was analyzed. In the conventional design, a vortex is created that reduces the degree of mixing by disturbing the flow. A novel design that generates flows in various directions is proposed to prevent vortex formation. To compare the two designs, the degree of mixing was calculated using the feed concentration at the top of the adsorbent layer. Computational fluid dynamics simulations were conducted to obtain the concentration data and were supported by experiments using residence time distribution analysis. The degree of mixing was improved regardless of which criterion was applied. [ABSTRACT FROM AUTHOR]