ABSTRACT In this paper, a probabilistic bi‐objective energy management system (EMS) model is proposed for an energy hub (EH) equipped with renewable energy sources such as photovoltaic and wind turbine connected to the main power grid, boiler, combined heat and power unit, along with thermal and electrical storages, in addition to power to gas (PtG) technology. The proposed EMS model aims to minimize operating costs and maximize the system flexibility index (SFI) while considering demand response programs (DRPs) and uncertainties in both load and generation. To solve the EMS model, a dynamic parameter multi‐objective cuckoo search (DP‐MOCS) algorithm is proposed. The high accuracy and superior performance of the proposed DP‐MOCS algorithm have been verified by solving standard ZDT benchmark functions and comparing the results with those of other multi‐objective optimization algorithms. The performance of the proposed EMS model for the EH is evaluated through simulations conducted in three sections, considering uncertainties and DRPs, as well as neglecting them. Simulation results demonstrate a reduction in costs and an increase in the EH's flexibility following the implementation of a DRP in the EMS. Incorporating DRP into the EMS has resulted in a 6.24% decrease in operating costs and a 3.41% increase in flexibility at the EH. Additionally, considering uncertainties in the EH led to a 2.25% rise in operating costs and a 2.72% decrease in SFI. Moreover, the proposed DP‐MOCS algorithm outperformed other algorithms in addressing the energy management problem.

Modeling and numerical simulation of coupled poromechanical problems with multicomponent compressible flow are of particular importance in many fields including shale and natural gas engineering, carbon dioxide sequestration and geotechnical engineering. In this paper, using the second law of thermodynamics, we rigorously derive a thermodynamically consistent model for multicomponent compressible flow in deformable porous media coupled with poroelasticity. The model herein takes molar densities as the primary unknowns rather than pressure and molar fractions as well as introduces fluid and solid free energies, so that it naturally follows an energy dissipation law. Additionally, the Maxwell-Stefan model of multicomponent diffusion is generalized as a multicomponent fluid-solid coupling model accounting for the solid deformation, which not only satisfies Onsager’s reciprocal principle but also yields a thermodynamically consistent poro-visco-elastic equation. For numerical simulation, we propose a novel energy stable and mass conservative numerical scheme for the model. We first design the semi discrete time scheme using the stabilized energy factorization approach to deal with the multicomponent Helmholtz free energy as well as subtle semi-implicit treatments for the coupling between multicomponent fluids and solids. A nontrivial treatment is the use of the discrete Gibbs Duhem equation substituting for the pressure gradient contributed by the multicomponent fluids in the solid mechanical balance equation, which establishes the thermodynamically consistent relation between fluids and solids at the discrete level. Based on the cell-centered finite volume method on staggered grids, the fully discrete scheme is constructed using the upwind strategy for both molar densities and porosity. The scheme is proved to preserve the discrete energy dissipation law and Onsager’s reciprocal principle as well as to conserve the mass of fluid components and solids. Numerical experiments are performed to confirm our theories, especially to demonstrate the good performance of the proposed scheme in energy stability and mass conservation as expected from our theoretical analysis.

Optimizing drilling parameters is essential for improving drilling efficiency and reducing operational costs in oil and gas engineering. This study presents an intelligent optimization approach for drilling parameters based on a hydraulic–mechanical specific energy (MSE) model. A time-series data fusion framework integrating Savitzky–Golay filtering, random forest, and hybrid anomaly detection was established to incorporate hydraulic parameters into the MSE model. The model parameters were further refined by coupling the rate of penetration (ROP) equation with a backpropagation (BP) neural network, achieving prediction accuracies of 70% and 90%, respectively. Field validation using 7,231 datasets from four wells revealed that weight on bit, rotary speed, and flow rate are the dominant factors influencing mechanical specific energy. Moreover, the simulated annealing algorithm was employed to globally optimize key parameters, resulting in an average improvement of 43. 34% in drilling efficiency. Compared with conventional MSE-based approaches, the proposed method innovatively integrates sliding window segmentation with the hybrid MSE (HMSE) technique, significantly enhancing time-series data processing. The developed multi-objective optimization model demonstrates superior prediction accuracy and adaptability under field conditions, providing a practical and effective tool for intelligent drilling parameter optimization.

A comprehensive review on the treatment technologies of on-road waste gas: Focusing on exhaust and asphalt VOCs reduction

Solidified natural gas (SNG) technology for biogas/biomethane storage: Current status and future outlook

This study presents a comprehensive analysis of global methane (CH<sub>4</sub>) emissions using advanced data exploration and machine learning techniques, with an emphasis on identifying key sectoral contributors, geographic emission hotspots, and the performance of mitigation technologies. Employing methods such as random forest regression, geospatial mapping, and multi-dimensional visual analytics, the research highlights the energy sector’s dominant role in methane output and reveals detailed emission patterns across U.S. states. The analytical framework includes time-series feature engineering, synthetic data augmentation for localized insights, and 3D surface modeling to examine the relationships between energy production levels, temporal trends, and emission intensities. The results provide actionable insights for policymakers by identifying critical points of intervention and advocating for the integration of artificial intelligence-driven exhaust after-treatment systems to reduce methane emissions. This work offers a scalable, reproducible approach for environmental monitoring and supports global decarbonization efforts in line with U.S. clean energy objectives. The random forest model used in this study achieved a mean absolute error of 2.71 and an R² score of 0.81, demonstrating strong predictive accuracy for methane emissions trends based on regional and sectoral data.

ABSTRACT Widespread adoption of technologies for greenhouse gas (GHG) emission mitigation is required to meet GHG reduction targets while maintaining levels of food production. In this context, improving our understanding of factors influencing behavioural change, including farmer views towards GHG mitigation, is required. Based on a representative sample of 526 farmers across various farm systems in the Republic of Ireland, this study takes an exploratory research focus and investigates the views of farmers towards their farm level GHG emissions by conducting a typology analysis that groups like-minded farmers together. Farmer views are first assessed on five-point Likert scales using nine statements. Principal component analysis (PCA) is applied to survey responses, revealing three components: one dealing with belief in ability and knowledge to reduce emissions, the second looking at environmental concern towards GHG emissions and the third gauging social mistrust felt by farmers as well as income prioritization. Subsequent cluster analysis shows four distinct farmer groups, which were then labelled based on their profile; Unconcerned, Ill-equipped, Concerned and Mistrusted. Finally, differences in farm and farmer characteristics across groups are examined using a series of statistical tests.

In the dual pursuit of achieving the global goal of carbon neutrality and curbing plastic pollution, chemical looping gasification (CLG) technology has emerged a key approach to integrate plastic waste recycling with H2-rich syngas production. This review systematically examines and compares the design strategy (support properties, active metal modification and bimetallic synergy) of oxygen carrier (OC) and the optimization progress of key process parameters (temperature, reactor staging and co-gasification of biomass) in the production of H2-rich syngas from plastic CLG. The findings demonstrate that support optimization and metal synergy can significantly regulate the syngas yield and its H2/CO ratio, while improving the cycle stability of OCs. Temperature and segmented CLG significantly improves the conversion rate and achieve flexible control of the H2/CO ratio. CLG of biomass and plastics can improve the yield and quality of H2-rich synthesis due to the complementary hydrocarbon and the coordinated conversion of tar. However, most studies at present use model plastics as fuel, which highlights the key shortcomings in understanding actual plastic waste, impurities, long-term CLG stability, and systematic economic evaluation. This review aims to provide guidance for the development of stable and selective OC systems and the optimization of CLG systems to achieve sustainable energy conversion of plastic waste.

Construction project scheduling and monitoring pose significant challenges in today’s dynamic business environment. This study investigates the implementation of two widely used project management techniques - the Critical Path Method (CPM) and the Project Evaluation and Review Technique (PERT) - in combination with Monte Carlo simulation for risk-based planning. The case study focuses on the PLTMG (Gas Engine Power Plant) Ambon-2 project. CPM provides a deterministic schedule with an estimated completion time of 65 weeks, representing an optimistic scenario. In contrast, the Monte Carlo simulation incorporates uncertainty and risk factors, indicating a 56.98% probability of project completion within 76 weeks and a 43.02% risk of delay. The results demonstrate the limitations of deterministic methods in high-risk environments and highlight the advantages of probabilistic simulations for more realistic project forecasting.

Against the background of global energy transformation and low-carbon development, numerous difficult-to-mine coal resources (e.g., deep, thin coal seams and low-quality coal) remain underdeveloped, leading to potential resource waste. This study systematically summarizes the feasibility of developing these resources via underground coal gasification (UCG) technology, clarifies its basic chemical/physical processes and typical gas supply/gas withdrawal arrangements, and establishes an analytical framework covering resource utilization, gas production quality control, environmental impact, and cost efficiency. Comparative evaluations are conducted among UCG, surface coal gasification (SCG), natural gas conversion, and electrolysis-based hydrogen production. Results show that UCG exhibits significant advantages: wide resource adaptability (recovering over 60% of difficult-to-mine coal resources), better environmental performance than traditional coal mining and SCG (e.g., less surface disturbance, 50% solid waste reduction), and obvious economic benefits (total capital investment without CCS is 65–82% of SCG, and hydrogen production cost ranges from 0.1 to 0.14 USD/m3, significantly lower than SCG’s 0.23–0.27 USD/m3). However, UCG faces challenges, including environmental risks (groundwater pollution by heavy metals, syngas leakage), geological risks (ground subsidence, rock mass strength reduction), and technical bottlenecks (difficult ignition control, unstable large-scale production). Combined with carbon capture and storage (CCS) technology, UCG can reduce carbon emissions, but CCS only mitigates carbon impact rather than reversing it. UCG provides a large-scale, stable, and economical path for the efficient clean development of difficult-to-mine coal resources, contributing to global energy structure transformation and low-carbon development.

In the coming years, the Russian Federation is expected to see growth in the transport of liquefied natural gas by road, as well as its use as a gas engine fuel. Accordingly, in the event of road traffic accidents involving such vehicles, firefighters must use procedures that differ from those established for normal conditions. This article provides an overview and analysis of recommendations for dealing with the consequences of accidents involving vehicles carrying liquefied natural gas. A summary of international experience and the results of domestic experimental studies allows us to identify key measures to ensure the safety of personnel and the public, requirements for fire extinguishing equipment, and limitations on their use. The theoretical significance of this work lies in its comprehensive systematization of approaches to responding to road accidents involving liquefied natural gas, and its practical significance lies in its formation of a basis for developing an action plan and guidelines for fire departments.

This research article aims to identify key factors influencing the effectiveness of university-industry research partnerships, drawing on the practices of the Skolkovo Institute of Science and Technology (Skoltech). Its relevance stems from the critical need to develop new instruments for leading Russian universities to achieve technological sovereignty and leadership. Methodologically, the study systematizes the macro-level context of scientific and industrial cooperation in Russia, highlighting distinctive features of Skoltech as an institution of a new type. This analysis is illustrated by two case studies of university-industry collaborations in the IT and oil and gas technology sectors. The findings indicate that Skoltech model, based on its Centers for Research, Education, and Innovation rather than traditional departments, coupled with flexible allocation of budget funds for problem-oriented research, has proven effective. It allows the university to propose commercially relevant research, moving beyond merely fulfilling existing corporate contracts. Targeted government programs and measures also provided for Skoltech crucial supplementary support for product commercialization. The novelty of the study lies in presenting Russia’s original experience with a university primarily focused on industrial research, and in detailing the mechanisms that enabled Skoltech to achieve high technology readiness levels. This article will be useful for science policy analysts, policymakers in science and technology, and leaders of universities and companies aiming to forge sustainable scientific and technological partnerships.

Abstract The gas engine heat pump is a heat pump system that uses natural gas as energy input and drives the compressor for the heat pump cycle. It has the characteristics of high primary energy utilization and environmental friendliness. In order to solve the problem of high energy consumption in traditional tobacco drying, this paper introduces a closed loop gas engine heat pump drying system (CGEHPDS). This study uses a simulation model based on MATLAB to evaluate the system performance, focusing on the energy efficiency, dehumidification, and operation characteristics of the tobacco drying process. The simulation results show that reducing the supply air temperature and increasing the air volume can improve COP and PER, but will increase energy consumption and power. The results of the tobacco drying cycle indicated that CGEHPDS showed high energy efficiency in the overall process of tobacco leaves, COP value maintained between 2.69 and 4.42 under most working conditions, PER value remained between 1.44 and 1.99 for most of the time, and SMER value was up to 1.85 kg/kWh. The operation law of the heat pump system changed with the change of the tobacco drying process. Through the energy-saving economic analysis of coal, gas unit price, and electric energy consumption, the system has potential advantages in energy saving and cost control. The research provides theoretical reference and data support for the design optimization and engineering application of CGEHPDS.

With the continuous growth of global energy demand, natural gas, as a clean fuel, has drawn people's attention, and natural gas hydrates, as a storage technology for natural gas, have also attracted much attention. However, the industrialization of natural gas hydrates is confronted with the slow formation kinetics of methane hydrates and the adverse environmental impacts caused by the extensive use of chemically synthesized surfactants. To overcome these difficulties, current research is focused on developing environmentally friendly green accelerators. In this experiment, maltodextrin, a biosurfactant, was utilized as a primary promoter to bind with methionine and phenylalanine, achieving a synergistic effect. The results indicated that maltodextrin significantly improved the slow formation kinetics of hydrates, and its promoting effect was closely related to the concentration. When the optimal concentration is 1000 ppm, the maximum gas storage capacity of the hydrate increases by 554.6% compared with the pure water system. Compared with the phenylalanine-maltodextrin complex system, the methionine-maltodextrin complex system has a better effect in promoting the formation of hydrates. The combination study of maltodextrin and two amino acids shows that 1000 ppm maltodextrin combined with 2000 ppm phenylalanine can shorten the hydrate induction time to 4.5 min (a reduction of 98.2% compared with the pure water system). The combination of 1000 ppm maltodextrin and 1500 ppm methionine increases the final gas storage capacity to 135.2 v/v (435.9% higher than the pure water system). It is worth noting that maltodextrin concentrations between 500 and 1000 ppm exhibit the best promoting effect. This series of research not only provides green and feasible solutions for addressing the dynamic bottlenecks and environmental risks of natural gas hydrate technology, but also offers significant support for achieving safer, more stable, and more environmentally friendly industrial applications of natural gas hydrates.

Adsorbed natural gas (ANG) technology represents a viable option for energy storage solutions. However, the enhancement of the storage performance is constrained by the thermal effect inherent to conformable ANG storage tanks for small dual-fuel ships. This research had developed a COMSOL numerical model for a 200 L conformable ANG storage tank packed with HKUST-1 to simulate the thermal effects resulting from the charging flow rates and temperature, charging modes. Based on the numerical calculation results, the effects of these charging conditions on the storage performance of the conformable storage tank were analyzed and evaluated. The findings suggested that the heat generated throughout the charging process was a crucial factor affecting both the cumulative storage amount and temperature fluctuations within the ANG system. Furthermore, the study revealed that a combination of low charging temperature and cyclic charging significantly mitigated the adverse effect of thermal effects, thereby improving charging efficiency, and enhancing the overall storage performance of the conformable ANG storage tank.

Particulate number emissions and particle micro-characteristics from natural gas engines: the role of lubricating oils

Advances in solidified methane and carbon dioxide storage: The potential of amino acids, biosurfactants, and nanoparticles as foam-free gas hydrate promoters.

Abstract Biomass gasification technology has been extensively researched around the world; however, there is a need to evaluate the current research landscape and evolutionary direction of research in the broader context of energy transition. A systematic bibliometric analysis of the Web of Science database was performed for articles that fall within the keywords ‘Biomass gasification’ and ‘Energy transition’. A total of 1498 articles were identified; after applying inclusion and exclusion criteria, 1196 articles were selected for final analysis. VOSviewer and Biblometrix were used for the study. Trends in biomass gasification and energy transition were identified as the initial (1994–2008), rise (2009–2018) and prosperity stages (2019 to date). A significant portion (47%) of publications were concentrated in 10 journals, including Renewable Energy , Energy and International Journal of Hydrogen Energy . Among the countries, China leads with 672 publications, followed by the US, India, and Italy. The prominent research areas are hydrogen production, process optimization, exergy analysis, tar reduction and life cycle assessment. Currently, the active area of research is the production of hydrogen‐rich gas through biomass gasification technologies and integrating bioenergy systems with carbon capture and storage to achieve sustainable energy transition goals.

The decarbonization of the chemical industry requires a carbon-neutral production of bulk chemicals. The feedstock (syngas) for synthesising such bulk chemicals can be produced by gasifying biomass or residues. This syngas produced by biomass gasification typically has a H2/CO ratio below 1. The synthesis requires an increased H2/CO ratio, e.g. Fischer-Tropsch a ratio of 2 - 2.1. Hydrogen addition and a water gas shift reactor are needed to meet this requirement and ensure a high carbon efficiency. Integrating an inline co-electrolysis into the process can be more efficient than parallel water electrolysis and additionally replace the water-gas-shift reactor. The oxygen-blown entrained flow gasification technology has been shown to produce syngas at high purity and conversion rates. This technology additionally enables a simple upscaling of the process. However, the efficiency and stability of such inline integration of the solid oxide cell (SOC) can be compromised by contaminants produced by the gasification process. Removing particles, sulphur and chlorine species at elevated temperatures (e.g. 350 °C) is mandatory to stabilise the SOC and downstream synthesis. However, for the fourth class of contaminants – complex aromatic hydrocarbons called tars - different impacts on the SOC operation were observed. Either the tars produced by the gasifier can harmlessly be reformed inside the SOC, or the tars cause carbon depositions, structural degradation and/or electrochemical degradation. The prediction of the tar’s impact on the SOC is additionally complicated by different tars exhibiting different behaviors. Therefore, a detailed comparison of multiple model tars at a relevant concentration for the entrained flow gasification is required.The main degradation mechanisms are structural degradation by carbon depositions or nickel dusting and a degradation of the catalytic activity and electrochemical performance. This work uses Ni-8YSZ commercial substrate material to investigate and compare the tar-induced degradation of multiple individual tar species. The circular 20 mm diameter substrate samples are operated at 700 °C under internal reforming conditions at a 50/25/10/10/5 % H2O/H2/CO2/CO/CH4 gas mixture. The individual model tars are dossed at a concentration of 0.5 g/Nm³ (dry basis). In single-cell and short-stack studies, electrochemical degradation was observed to happen in parallel to the degradation of the chemical activity of the cell. This observation is utilised in the test rig: If no degradation of the catalytic performance of the substrate layer occurs, no electrochemical degradation of a full cell is expected. This simplifies the study and enables a fast screening of the different tars. The chemical activity of the substrate material is monitored via the steam methane reforming reaction with an off-gas analysis. Scanning electron microscopy is used for post-mortem analysis of the substrate sample. The study compares the investigated tars, exploring the influence of their functional groups on the degradation process.

ABSTRACT Increasing complexity in the British power sector, the UK 2050 “Net Zero” strategy, and proposed changes to pricing regulation in order to separate gas-based and low-carbon electricity prices—so-called “dual pricing”—provide opportunities to study the sector using an agent-based model of electric power generation technology investment decisions. This paper develops a novel agent-based model at the power plant level to produce a spatial representation of the British (GB) electricity grid based on the location of individual power plants. We then examine the impact of individual plant attributes on low-carbon investment decisions while allowing for geographical resource availability, market and policy uncertainties. Our model results show that the new proposed dual pricing mechanism provides a massive reduction in investment cost through favouring wind electricity penetration in the evolution of the GB electricity system, but achieves lower carbon reductions because of the significant role of gas technology in addressing the intermittency of wind electricity. This is in contrast to the current, marginal pricing system which promotes capacity from nuclear technology. Our results also suggest that the new dual pricing regime would reduce CO2 emission levels by only 55% at 2050, compared to an 80% reduction under the current marginal pricing system.