Ethylene production via naphtha steam cracking is one of the most energy-and carbon-intensive processes in the petrochemical industry. As global efforts toward carbon neutrality accelerate, electrification has emerged as a promising pathway for reducing reliance on fossil fuel combustion. This study evaluates the sustainability of electrified naphtha cracking, considering both electrified steam cracking and plasma arc cracking, using six electricity sources and multiple methane utilization pathways. Industrial-scale process models, supported by pilot-scale data for plasma cracking, were developed to obtain detailed material and energy balances. Comprehensive techno-economic analysis (TEA), life cycle assessment (LCA), and carbon avoidance cost (CAC) evaluation were performed. The results show that plasma cracking achieves higher ethylene selectivity but requires a substantially higher energy input than electrified steam cracking. Economically, electrified steam cracking with methane-to-blue-hydrogen conversion powered by wind achieved the lowest levelized cost of ethylene (LCOE) ($1.66/kg ethylene) and a negative CAC (-$89.30/tonne CO2-eq), indicating simultaneous cost savings and emission reduction. Environmentally, the lowest emissions occurred in a case study with plasma cracking with methane-to-blue-hydrogen conversion powered by hydropower, achieving an 80.32% reduction relative to that of conventional naphtha steam cracking. Monte Carlo simulation confirmed the robustness of this finding, with the most cost-effective configuration showing an 86.5% probability of CAC below the current carbon price. Overall, this study identified renewable-powered electrified steam cracking combined with carbon-capture-enabled methane utilization as the most cost-effective near-term pathway for carbon-neutral ethylene production, while highlighting the efficiency improvements needed for future plasma cracking technologies.

This study presents a comprehensive computational pipeline to identify and evaluate potential stabilizing mutations for the coiled-coil protein-protein interaction between methyl-CpG-binding domain protein 2 (MBD2) and transcriptional repressor p66-alpha (p66 alpha). The pipeline begins with the BeAtMuSiC program, which employs statistical potentials derived from known structures to predict candidate stabilizing mutations at the protein-protein interface. Out of 565 potential mutations, 10 single-point mutations (K149I, K163I, A237F, K149L, K149M, K163L, R166M, R166W, K163F, and E155L) with the highest binding affinity were selected for further evaluation using rigorous alchemical free energy calculations. These alchemical simulations conducted using the double-system/single-box method, predicted changes in binding free energy (Delta Delta G) upon mutation while maintaining charge neutrality. The Crooks-Gaussian intersection technique was employed to analyze the results, identifying K149I, K149L, and K163L as potentially enhancing binding affinity the most, while mutations like K163F, A237F, and E155L were predicted to destabilize the interaction significantly. Complementary conventional Molecular Dynamics Simulations provided further support for the alchemical predictions, revealing decreased flexibility, increased contacts, and more compact structures for the predicted stabilizing mutants compared with the wild-type complex. Additionally, Molecular Mechanics Poisson-Boltzmann Surface Area (MM/PBSA) binding free energy calculations were performed, and their results were consistent with the direction of free energy change predicted by the alchemical approach. This multifaceted computational pipeline, combining predictive methods, alchemical simulations, and conventional analyses, offers valuable insights into modulating the binding affinity of the MBD2-p66 alpha coiled-coil interaction. The identified stabilizing mutations can create numerous opportunities across biotechnology, biomedical research, and synthetic biology.

Lignocellulosic biomass is one of the viable solutions to alleviate the global warming. However, the limited utilization of biomass majorly focused on cellulose and hemicellulose restricts the economic and environmental feasibilities. To cope with this issue, we proposed an integrated process of co-producing 1,6-hexanediol (1,6-HDO) with tetrahydrofuran and adipic acid from biomass, referred to as Strategy A. To compare the impacts of lignin upgrading and feedstock, Strategy B, which co-produces tetrahydrofuran alone, and Strategy C, which is the traditional route to produce 1,6-HDO from fossil fuels, were used. Heat networks are also designed to reduce operating costs and indirect carbon emissions due to energy consumption, saving 87% and 83% of the heat and cooling requirements, respectively, in Strategy A. The market competitiveness of Strategy A was evaluated by determining the minimum selling price through techno-economic analysis, and sustainability was thoroughly investigated by quantifying the environmental impacts through both midpoint and endpoint life-cycle assessments (LCAs). Strategy A was found to be the most favorable both economically (US$3,402/ton) and environmentally (-26.9 kg CO(2 )eq.). This indicates that lignin valorization is not only economically but also environmentally preferred. Finally, changes in economic and environmental feasibilities depending on economic, process, and environmental parameters were investigated using sensitivity and uncertainty analyses. The results of these analyses provide valuable insight into bio-based chemical production. (c) 2024 Science Press and Dalian Institute of Chemical Physics, Chinese Academy of Sciences. Published by ELSEVIER B.V. and Science Press. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

The analysis of South Korea's hydrogen economy roadmap 2040, particularly in response to the COVID-19 pandemic, reveals profound insights with global implications. South Korea's strategic pivot towards hydrogen serves not only as a national blueprint for achieving a sustainable, low-carbon future but also as a case study for the international community. Despite challenges posed by the pandemic, the Korean government's unwavering commitment to hydrogen initiatives underscores its critical role in economic recovery and environmental objectives. Key findings include South Korea's prioritization of hydrogen as a key element of its energy policy, aiming to produce 6.2 million tons of hydrogen per year by 2040. The analysis also highlights challenges in balancing economic recovery with sustainable development, with a 3.7% GDP drop and 457,000 job losses in 2020 due to COVID-19. Additionally, the manuscript underscores the practical implications of South Korea's experience for other nations, emphasizing the importance of leadership, policy coherence, innovation, infrastructure, and international cooperation in driving the hydrogen transition. The challenges brought by the pandemic have sparked innovation, emphasizing the crucial need to establish a sustainable and resilient hydrogen future with far-reaching implications for economic, environmental, and geopolitical landscapes. The manuscript concludes by asserting South Korea's position as a leader in the global hydrogen economy and the need for continued investment, policy innovation, and international cooperation to realize hydrogen's potential in the global energy transition.

The global plastic waste crisis demands urgent attention due to its harmful impact on ecosystems. Chemical recycling of plastic waste offers a perspective aligned with circular economy principles and carbon neutrality, utilizing plastic waste as a carbon source to replace fossil-based feedstock. This study conducts techno-economic and life cycle assessments, based on experimental data, to demonstrate the chemical recycling of expanded polystyrene (EPS) waste through depolymerization to produce recycled styrene monomer (r-SM). Process simulations were carried out to obtain mass and energy balances and equipment sizes. The simulation outcomes served as inputs for economic and life cycle assessments. Three cases were evaluated: Case 1) low-grade r-SM (>95 % purity) without using a solvent for dissolution, Case 2) low-grade r-SM (>95 % purity) by using THF as a solvent for dissolution, and Case 3) a high-grade r-SM (>99 % purity) without using a solvent. Case 3 offers the most economically viable option, with a minimum product selling price (MPSP) of $1.06/kg, comparable to virgin styrene monomer (v-SM). Sensitivity analysis identified EPS waste costs as the biggest influence on MPSP. The life cycle assessment (LCA) showed that all cases have a lower Global Warming Potential (GWP) than the production of v-SM, with Case 1 achieving an 89 % GWP reduction. Energy consumption is identified as the primary contributor to GWP results. These findings reveal further research prospects to explore in the pursuit of implementing EPS waste recycling.

Polyhydroxybutyrate (PHB) is a biopolymer that accumulates in cyanobacteria and other microalgal species. This compound serves as an intracellular carbon storage product in the microorganisms formed during photosynthesis. The microalgal strain Chlorella sorokiniana is known to produce PHB. Eco-friendly bioplastics, such as PHB, have the potential to replace conventional plastics. Lipids in microalgae can also be used to produce biofuels, such as diesel blendstock (DB). In this study, we developed an integrated process for growing microalgae and using them to produce PHB and DB. This techno-economic analysis explores the financial profitability of using open raceway ponds compared to photobioreactors to grow microalgae with nutrient deficiency, with sodium acetate as the carbon source. The heat integration applied in the process decreased the energy requirement, leading to 74.2 and 50.5% reduction in heating and cooling requirements, respectively. The economic aspect was addressed by determining the minimum selling price of PHB, which was estimated to be $8830 per ton compared to the market price of $8500-19,600/ton, demonstrating the reasonableness of this process. This study incorporated a life cycle assessment cradle-to-gate approach to evaluate the environmental impact, considering its life cycle from raw material acquisition to end-of-manufacturing.

Accurate retention time (RT) prediction in liquid chromatography remains a significant consideration in molecular analysis. In this study, we explore the use of a transformer-based language model to predict RTs by treating simplified molecular input line entry system (SMILES) sequences as textual input, an approach that has not been previously utilized in this field. Our architecture combines a pretrained RoBERTa (robustly optimized BERT approach, a variant of BERT) with bidirectional long short-term memory (BiLSTM) networks to predict retention times in reversed-phase high-performance liquid chromatography (RP-HPLC). The METLIN small molecule retention time (SMRT) data set comprising 77,980 small molecules after preprocessing, was encoded using SMILES notation and processed through a tokenizer to enable molecular representation as sequential data. The proposed transformer-LSTM architecture incorporates layer fusion from multiple transformer layers and bidirectional sequence processing, achieving superior performance compared to existing methods with a mean absolute error (MAE) of 26.23 s, a mean absolute percentage error (MAPE) of 3.25%, and R-squared (R2) value of 0.91. The model's explainability was demonstrated through attention visualization, revealing its focus on key molecular features that can influence RT. Furthermore, we evaluated the model's transfer learning capabilities across ten data sets from the PredRet database, demonstrating robust performance across different chromatographic conditions with consistent improvement over previous approaches. Our results suggest that the hybrid model presents a valuable approach for predicting RT in liquid chromatography, with potential applications in metabolomics and small molecule analysis.

The rapid expansion of energy storage integration has not provided sufficient time to strengthen and expand the transmission and distribution network. This issue can lead to PCC congestion in green multi-microgrid (MMG) systems. In these systems, microgrids operate independently and connect to the grid at a point of common coupling (PCC) without sharing operational data with neighboring microgrids. To address this issue, this paper proposes a bi-level optimization model designed to reschedule hydrogen storage systems. The first level allows each microgrid to optimize its energy transactions with the grid and communicates any unbalanced energy to the second level, where a hydrogen management system (HMS) is introduced. The HMS optimizes virtual hydrogen prices to address the PCC congestion and maximize the MMG’s profit. These virtual prices are then sent to the first level, allowing the microgrids to reschedule the hydrogen storage systems based on these virtual prices. Finally, the MMG’s profit is fairly allocated among the microgrids using the Shapley value method. The proposed method’s effectiveness is demonstrated using simulations, which show a six percent increase in MMG profit compared to scenarios that only share PCC capacity while maintaining the data privacy of all the involved microgrids.

In recent literature, the value of electric vehicles (EVs) for the resilience enhancement of urban microgrids has been shown. Furthermore, on a larger scale, there has been a growing recognition of the potential of EV cooperation in enhancing the overall resilience of smart cities. To this end, the city can be partitioned into a set of blocks, each encompassing buildings. Within each block, EV traveling time can be ignored. As a step forward, this study presents a Preventive Energy Management (PEM) strategy along with a rescheduling procedure by cooperation of EVs, local distributed energy resources (DERs), and buildings in different city blocks. Based on the available information related to the amount of curtailed loads, two cases are modeled and studied. In the proposed PEM strategy, EV owners’ opt-in preferences such as arrival and departure times, and the city block in which they are willing to give energy services are modeled. As a more realistic consideration, the proposed model does not consider the buildings’ load as a lumped load, instead the PEM strategy is designed to consider each of the buildings separately. The resulting optimization model is flexible enough to enable EVs to switch from one building to another to provide energy in different time slots. By applying disjunctive-constraint-based transformation, the model is recast as a Mixed Integer Linear Programming (MILP) that could be efficiently solved by commercial optimization solvers. The proposed approach is applied to a benchmark and the results are analyzed. According to the results, using EVs in the PEM strategy has been proven to be effective and the importance of the length of the period of service and opt-in preferences for optimal scheduling are highlighted.

Rapid advancements in digital technologies have accelerated global change, underscoring the critical role of resilience in addressing the escalating energy, economic, and environmental challenges. This paper investigates the effects and mechanisms of the digital economy on energy, economic, and environmental resilience within the context of these challenges. By utilizing panel data from 66 countries spanning the period from 2000 to 2020, this analysis employs robust panel data models and incorporates tests such as the Hausman and Leamer tests, and exploratory factor analysis. The results reveal a notable positive impact of the digital economy on resilience across various countries and time periods. However, when it comes to carbon emissions, a more intricate pattern emerges, suggesting a negative influence on resilience in environmental, energy, and economic domains. Interestingly, countries with below-average carbon emissions show more positive effects on economic resilience due to the digital economy. On the other hand, the effect of the digital economy on energy resilience is less prominent in below-average carbon-emitting nations, while carbon emissions have a more significant impact within this subgroup. Above-average carbon-emitting countries experience limited effects of the digital economy on environmental resilience, while below-average carbon-emitting countries face challenges with significant carbon emissions impacting their environmental resilience.

The promotion of green hydrogen as a clean and sustainable energy carrier has garnered significant attention in recent years due to its potential to mitigate climate change and reduce dependence on fossil fuels. This paper presents a novel approach to promote hydrogen production through a shared Multi-energy System (MES) within residential microgrids. The proposed system leverages excess rooftop photovoltaic (PV) production to generate hydrogen using an electrolyzer and a Hydrogen Storage System (HSS). By adopting an optimization model, the primary objective is to maximize overall household profit by considering their optimal energy trading with the grid and neighborhood. To promote participation in green hydrogen production, an energy credit mechanism is introduced, allowing households to use credited energy during peak hours to reduce energy costs based on their participation in hydrogen production. This mechanism not only promotes the financial value of rooftop PVs but also incentivizes households to contribute to hydrogen production. Drawing from various studies in smart energy systems, the concept of a MES has been successfully implemented in different contexts. Building on this foundation, our paper introduces a shared MES tailored specifically for residential hydrogen production. In addition to proposing this innovative approach, the paper contributes to the existing literature by introducing the concept of energy credits as a reward mechanism for household contributions to hydrogen production. The results of implementing the proposed model on a case study for 40 houses show that the contribution of households to hydrogen production is significant, while their profit has been increased by benefiting from credited energy.

Hydrogen supply chain (HSC) consists of various production, conditioning, transportation, storage, and distribution processes, all of which require extensive computational resources for precise modelling. In this context, artificial intelligence (AI) is emerging as a pivotal tool for addressing model-based decision-making challenges, courtesy of its rapid and efficient computational capabilities. This paper proposes a comprehensive HSC model consisting of an economic policy planner, a wholesale hydrogen market, a power supply system, a hydrogen distribution system (HDS), and hydrogen refueling stations (HRSs). It leverages an AI circular hydrogen economist approach based on a hierarchical deep multi-agent reinforcement learning (MARL) algorithm, offering a new alternative to traditional multi-objective bi-level optimization platforms. The model incorporates green, blue, and gray hydrogen production processes as viable hydrogen production pathways, with the hierarchical MARL’s agents representing the HDS and HRSs at two decision-making levels. Energy requirements are met through a combination of on-site renewable energy sources, the main power grid, or distributed power generation systems. Several HSC scenarios are examined with respect to various combinations of green hydrogen supply rates and carbon credits granted to them under optimum conditions. The results showed that the developed hierarchical MARL has the potential to replace mathematical programming (MP), uncovering a new economic-environmental trade-off between profit of HDS and operational costs of HRSs. Notably, green hydrogen transactions exponentially increase within the supply chain as the carbon credits exceed $1 per kilogram of hydrogen. While this research focuses on optimizing daily operations within the HSC, future efforts can aim to extend this optimization to forecasted annual operations.

Day-ahead energy management systems focus on optimizing resource scheduling on a daily basis, which may not adequately address seasonal load or price fluctuations. Targeting these long-term fluctuations in day-ahead scheduling, this paper introduces a two-stage optimization methodology specifically designed for day-ahead scheduling with long-duration hydrogen storage systems (HSS) that effectively eliminates the need for scenario-reduction techniques by dividing the long-term scales into short-term ones. As the amount of stored hydrogen in the storage tank affects operational scheduling on consecutive days, the first stage introduces a new variable to represent variations in the stored hydrogen amount, effectively decoupling consecutive days. Subsequently, the second stage employs a developed active set algorithm. This algorithm adds hydrogen storage tank constraints to the objective function to ensure that the stored hydrogen amount does not exceed the tank’s capacity limits on any day. Using real-world data from South Australia State, simulation results validate the proposed algorithm’s effectiveness and demonstrate that employing large storage tanks within an HSS is viable for long-duration applications.

Nervousness-aware rescheduling is essential in maximizing the profitability and stability of processes in manufacturing industries. It involves re-optimization to meet scheduling goals while minimizing deviations from the base schedule. However, conventional mathematical optimization becomes impractical due to high computational costs and the inability to handle real-time rescheduling. Here, we propose an online rescheduling agent trained by explorative reinforcement learning that autonomously optimizes schedules while considering schedule nervousness. In a static scheduling environment, our model consistently achieves over 90% of the cost objective with scalability and flexibility. A computational time comparison proves that the reinforcement learning methodology makes near-optimal decisions rapidly, irrespective of the complexity of the scheduling problem. Furthermore, we present several realistic rescheduling scenarios that demonstrate the capability of our methodology. Our study illustrates the significant potential of reinforcement learning methodology in expediting digital transformation and process automation within real-world manufacturing systems.

The government’s support for rooftop photovoltaic systems has significantly increased their installed capacity, leading to the creation of numerous independent microgrids (MGs). However, multi-microgrids (MMGs), which comprise these MGs, often do not share operational data with neighboring microgrids. This lack of information can lead to congestion at the common coupling point, particularly at midnight when energy prices are low, an issue that this paper addresses for the first time. This paper will break the day into two scales: daytime and nighttime. Then, to manage the congestion, it will introduce a nighttime energy storage charging market managed by an aggregator. This charging market has two levels: at the first level, each MG will optimize its energy storage charging bids, and at the second level, the aggregator will form a supply curve to settle the nighttime charging market to minimize the costs of MMGs while managing congestion. To allocate congestion costs, the aggregator will form supply and bid curves for all possible coalitions. Then based on the ’pay as cleared’ market concept, it will allocate these costs/profits using a developed Shapley value. A comparison of the proposed method with the centralized method indicates that the proposed methodology yields an optimal solution while the privacy of MGs is required. The effectiveness of this methodology is validated through a case study using real data from Australia.

Congestion management involves controlling and optimizing energy flow to ensure efficient and reliable operation in energy systems. The objective of this paper is to effectively manage congestion in networked microgrids by balancing energy supply and demand, thereby preventing overloads and ensuring a stable and resilient energy system. One approach to achieve this is through Model Predictive Control (MPC), which can regulate power flow while considering realistic constraints and system dynamics. The results of applying linear MPC for congestion management in networked microgrids are promising when all parameters are deterministic. However, introducing uncertainty into the model poses challenges that linear MPC cannot address. This paper introduces a Chance-Constrained Model Predictive Control (CC-MPC) method to tackle this problem through mathematical reformulation and stochastic optimization. The decision to use CC-MPC over robust MPC or tube MPC was based on the unknown uncertainty of the problem, making stochastic MPC a more suitable option. The results from provided numerical examples demonstrate that the proposed method not only accurately predicts demand response but also effectively manages congestion in both small-scale and large-scale networked microgrids, even while accounting for the effects of uncertainty.

Quantitative structure-retention relationship (QSRR) modeling has emerged as an efficient alternative to predict analyte retention times using molecular descriptors. However, most reported QSRR models are column-specific, requiring separate models for each high-performance liquid chromatography (HPLC) system. This study evaluates the potential of machine learning (ML) algorithms and quantum mechanical (QM) descriptors to develop QSRR models that can predict retention times across three different reversed-phase HPLC columns under varying conditions. Four machine learning methods—partial least squares (PLS) regression, ridge regression (RR), random forest (RF), and gradient boosting (GB)—were compared on a dataset of 360 retention times for 15 aromatic analytes. Molecular descriptors were calculated using density functional theory (DFT). Column characteristics like particle size and pore size and experimental conditions like temperature and gradient time were additionally used as descriptors. Results showed that the GB-QSRR model demonstrated the best predictive performance, with Q2 of 0.989 and root mean square error of prediction (RMSEP) of 0.749 min on the test set. Feature analysis revealed that solvation energy (SE), HOMO–LUMO energy gap (∆E HOMO–LUMO), total dipole moment (Mtot), and global hardness (η) are among the most influential predictors for retention time prediction, indicating the significance of electrostatic interactions and hydrophobicity. Our findings underscore the efficiency of ensemble methods, GB and RF models employing non-linear learners, in capturing local variations in retention times across diverse experimental setups. This study emphasizes the potential of cross-column QSRR modeling and highlights the utility of ML models in optimizing chromatographic analysis.

The toluene (TOL)-methylcyclohexane (MCH) system is one of the viable solutions because of its high stability and high hydrogen storage capacity (6.2%). However, the high volatilities of TOL and MCH and the accumulative byproducts make it difficult to transport hydrogen. Considering these limitations, we developed a new strategy introducing an extraction column and pressure swing adsorption with heat integration to reduce the required energy utilities. Furthermore, a comprehensive system-level analysis was conducted through an application example of the transport of hydrogen from Australia to Korea. The minimum transport cost of hydrogen was determined to be $2.17/kg-H-2 via techno-economic analysis. Sensitivity and uncertainty analyses revealed the influence of the economic and process parameters. Finally, a life cycle assessment was conducted to compare the environmental impact (EI) of each part. Although dehydrogenation is more energy-demanding than hydrogenation, hydrogenation has larger EIs for some factors including fossil resource scarcity (13% larger) and water consumption (746% larger), due to the toluene and hydrogen makeup. Furthermore, we compared changes in the EIs in the energy sources. This study can provide insights into the optimization and decision-making of hydrogen supply chains to revitalize the hydrogen economy.