Design Of Production Systems Research Articles

New permeability model considering multiscale migration mechanism of deep coalbed methane and its application

Deep coalbed methane (CBM) flow exhibits various transportation mechanisms within the multiscale pore structures of reservoirs, including continuous flow, Knudsen diffusion, and surface diffusion. Current research predominantly emphasizes the effects of individual or partial flow mechanisms and single-factor influences on the multiscale migration of CBM. We proposed a new apparent permeability model that integrates multiple flow mechanisms to enhance our understanding of the factors governing CBM flow in complex fractured networks. This model accounted for stress sensitivity, adsorbed gas desorption, and matrix shrinkage. By assigning appropriate weights to different flow mechanisms, the model yielded a more accurate representation of the deep CBM apparent permeability, avoiding the overestimation resulting from the linear superposition of diverse migration mechanisms. Our findings indicated that the apparent permeability was positively correlated with compressibility and negatively correlated with the tortuosity and Poisson's ratio. In the presence of the adsorbed gas, the apparent permeability of organic matter showed heightened sensitivity to formation pressure, rock compressibility, and tortuosity. However, the impacts of these factors became less pronounced when the pressure differential was small. The proposed model was applied to the flow simulations for a multi-fractured horizontal well within a deep coal reservoir characterized by a complex fracture network. The simulation results agreed well with the production data. We found that continuous flow was the dominant contributor to the apparent permeability of organic and inorganic matter within the coal rock, followed by Knudsen diffusion and surface diffusion. This study provided insights into the evolution of apparent permeability of CBM during development and offered valuable guidance for the analysis of CBM production dynamics, productivity forecasting, and production system design.

Optimal Design of a Modular Production System for Renewable Methanol to Mitigate Carbon Dioxide Emissions from Coal-Fired Power Plants

Optimal Design of a Modular Production System for Renewable Methanol to Mitigate Carbon Dioxide Emissions from Coal-Fired Power Plants

Soil arthropod diversity, richness, and abundance in agroecological and conventional cotton production systems in Chaco, Argentina

In Argentina, agroecology has grown in last years as a scientific paradigm that seeks to design and evaluate agroecosystems considering sustainability, complexity, and uncertainty. Diversity is a key factor in the design and management of production systems and a necessary component for conservation biological control and the reduction of agrochemicals use. Cotton (Gossypium hirsutum) crop in northern Argentina is usually managed with high load of agrochemicals: agroecological production arises as an alternative. This study evaluated the diversity, richness, and abundance of soil arthropods using pitfall traps in two experimental cotton plots under conventional (CONV) and agroecological management (AE) in Chaco, Argentina. AE system presented higher diversity and richness of predators compared with CONV, even when natural preparations were used for pest control. The phytophagous arthropods showed higher diversity and richness in CONV, even when pyrethroid insecticides were applied for pest control and preventively. The abundance in AE was lower for predators and higher for phytophagous arthropods. Agroecological production of cotton can be a tool that favors conservative biological control and an alternative for areas where protecting the health of farmers and the ecosystem is a priority.

Stability analysis and multi-objective optimization of methanol steam reforming for on-line hydrogen production

The potential solution to hydrogen storage and transportation challenges is provided by liquid fuel-based on-line hydrogen production. An online hydrogen production device has been constructed and the stable output of hydrogen at 6.8 bar under dynamic conditions of 6–13 Nm3/h has been achieved. To achieve optimal economic and thermodynamic performances, the mathematical models for mass, energy, and techno-economics are constructed based on field test data. The Non-Dominated Sorting Genetic Algorithm is employed to update temperature splitting points between multi-stage heat exchangers, targeting multi-objective optimization of heat exchange area, system energy efficiency, and annual average cost. Pareto optimal solutions indicate that such optimization can improve system energy efficiency by 0.03%–3.76% and decrease the hydrogen production cost from 0.004 $/kg to 0.599 $/kg. This work demonstrates the feasibility and effectiveness of multi-objective optimization for online hydrogen production system design and offers important guidance for decision-making in heat exchange network schemes.

Construction of Smart Clothing Service System for the Health and Well-Being of the Aging Community in a Sustainable Society

Objective: The phenomenon of global aging is becoming more and more severe, and security services such as nursing and guardianship for the elderly have become the focus of social attention. According to the data released by the World Health Organization, by 2030, one out of every six people will be over 60 years old. By 2050, the world population will be 2.1 billion over 60 years old, and the number of people over 80 years old will reach 460 million. This paper aims to monitor the health and services of aging clothing intelligently for the health and well-being of the aging community in a sustainable society, starting from the physiological and psychological needs of the new elderly. Theoretical Framework: In the age-appropriate smart clothing service system, the critical link is to construct the human-computer interaction mode of clothing and wearable devices, establish a design paradigm from single interaction to multi-interaction, and form a service terminal based on smart wearable. Method: This paper designs an aging smart clothing design system using field research, questionnaires, interviews, etc., analyzes the aging smart wearable clothing evaluation system, and constructs aging smart clothing that meets the actual needs of health and well-being for the elderly in a sustainable society. Results and Discussion: In this research, The results obtained sustainable design theories and methods combined with interdisciplinary research tools as the basis of data analysis from the aging intelligent clothing product design system, age-appropriate smart clothing service system, and age-appropriate smart clothing evaluation system. Research Implications: Population aging has been the basic national condition of our country for quite a long time in the future, and "healthy aging" is a strategic measure put forward by the government around the intelligent care service system for the elderly, communities, and institutions. Important direction. Among them, how to build an age-appropriate smart wearable product service system has become the main development content. Therefore, its intelligence and synergistic combination with terminals such as communities, caregivers, and hospitals will become the trend of smart elderly care. Originality/Value: This study contributes to Construct a systematic design of a new intelligent wearable clothing service system for the elderly, which is a great of significance for improving the service quality index of the health and well-being of the elderly. At the same time, it is of great value to expand and deepen the concept of sustainable design.

UNLOCKING THE POTENTIAL OF BIOENERGY: A SUSTAINABLE SOLUTION FOR ENERGY SECURITY, WASTE MANAGEMENT, AND ENVIRONMENTAL CONSERVATION

The study was carried out to generate biogas from fresh cowdung. Proximate analysis on the cow-dung sample revealed moisture content to be 60.53%, Ash content 11.01%, total solids 39.47%, volatile matter 10.78%, protein content 12%, nitrogen content 1.6%, carbohydrate 5.68% and fixed carbon 17.68%. The pH and Temperature were measured daily at 2:00pm, the temperature ranged from 30OC-38OC and the pH range was 6.7 - 7.2. The gas produced was measured daily by calculating the volume with the height increased daily up to 36 days. The results showed a typical biogas production curve, consisting of lag, acceleration, maturation, and decline phases. The optimal retention time for biogas production is identified as 20-30 days, during which biogas yields are highest. The findings indicate that microorganisms require an initial adaptation period (Days 1-3) before biogas production commences, followed by a significant increase in production (Days 11-20) and a stable production rate (Days 21-30). A decline in production is observed after 30 days. This study highlights the importance of retention time in biogas production and demonstrates the need for monitoring production kinetics to optimize retention time for specific substrates and microbial communities. The results have implications for the design and operation of biogas production systems.

Design and optimization of hybrid renewable energy systems for hydrogen production at Aksaray University campus

In this study, an off-grid HRES is proposed to ensure the electricity demands of the campus in a reliable, cost-effective, and non-polluting way for Aksaray University to have a sustainable and green campus. Within this framework, three HRESs were designed and compared using HOMER Pro software to find the optimum HRES, using a combination of different components related to zero carbon emissions and fully renewable energy sources, including transportation with environmentally friendly hydrogen fuel cell buses for students, academics, and staff. According to the optimization results obtained for the various configurations, the optimum HRES has a net cost of $20.3 million for the 25-year project life, with annual costs of $1.57 million. The levelized cost of electricity of the proposed system, represented by Scenario III, is calculated to be 0.327$/kWh. The PV panels produce 4,758,497 kWh/year at a levelized cost of 0.0404$/kWh, while the wind turbines produce electricity at a levelized cost of 0.0625$/kWh. The optimal system includes a 2000 kW electrolyzer that produces 73,061 kg of hydrogen annually, with a consumption rate of 46.4 kWh/kgH2. The hydrogen tank has an energy reserve of 83,333 kWh with a storage capacity of 2500 kg. The results indicate that Scenario III is a robust, cost-effective, and environmentally friendly energy solution for the campus, paving the way for a greener future. Furthermore, the proposed HRES model provides a practical framework that can influence campus energy policies and potentially serves as a model for other educational institutions that are interested in implementing sustainable energy solutions.

Design and Optimization of a Chemical Looping Hydrogen Production System Using Steam Reforming for Petrochemical Plants.

Industrial pollution is a significant environmental concern, and researchers are seeking solutions to minimize its impact. Chemical looping combustion is an efficient technique to decrease CO2 emissions. This study delves into its application within petrochemical plants for hydrogen production. In addition to its high efficiency in hydrogen production, this method yields pure CO2 and nitrogen. The design features four reactors utilizing iron and nickel metals as oxygen carriers. A model in Aspen Plus was developed to simulate this design and evaluate its performance. Aspen Plus uses RGIBBS submodel which works based on minimization of the Gibbs free energy. The research findings indicate that this design significantly boosted the hydrogen production rate from 3380 kmol/h in conventional steam reforming reactors and 4258 kmol/h in the three-reactor chemical looping combustion setup to 4408 kmol/h. Lowering the temperatures of the iron and nickel fuel reactors, as well as the iron steam reactor, reducing the airflow rate, and increasing the pressure of the iron steam reactor all led to increased hydrogen production. Conversely, changing the pressure of the nickel fuel reactor had minimal impact. The inlet steam flow rate exhibited a nonlinear effect, initially increasing and then decreasing the hydrogen production rate.

PULPO: A framework for efficient integration of life cycle inventory models into life cycle product optimization

AbstractThis work presents the PULPO (Python‐based user‐defined lifecycle product optimization) framework, developed to efficiently integrate life cycle inventory (LCI) models into life cycle product optimization. Life cycle optimization (LCO), which has found interest in both the process systems engineering and life cycle assessment (LCA) communities, leverages LCA data to go beyond simple assessments of a limited number of alternatives and identify the best possible product systems configuration subject to a manifold of choices, constraints, and objectives. However, typically, aggregated inventories are used to build the optimization problems. Contrary to existing frameworks, PULPO integrates whole LCI databases and user inventories as a backbone for the optimization problem, considering economy‐wide feedback loops between fore‐ and background systems that would otherwise be omitted. The open‐source implementation combines functions from Brightway2 for the manipulation of inventory data and pyomo for the formulation and solution of the optimization problem. The advantages of this approach are demonstrated in a case study focusing on the design of optimal future global green methanol production systems from captured CO2 and electrolytic H2. It is shown that the approach can be used to assess sector‐coupling with multi‐functional processes and prospective background databases that would otherwise be impractical to approach from a standalone LCA perspective. The use of PULPO is particularly appealing when evaluating large‐scale decisions that have a strong impact on socioeconomic systems, resulting in changes in the technosphere on which the background system is based and which is often assumed constant in standard LCO approaches regardless of the decisions taken. This article met the requirements for a gold‐gold JIE data openness badge described at http://jie.click/badges.

Advancing heavy manufacturing

Advancing heavy manufacturing The main objective of ENGINE is to develop a first-time-right (FTR) and zero-defect metal product design and manufacturing system, then demonstrate it on the marine engine supply chain. Our ambition is to increase the competitiveness of industry and SMEs, reduce manufacturing defects and waste, create new business cases and improve employee well-being.

Flexible design of renewable hydrogen production systems through identifying bottlenecks under uncertainty

Renewable hydrogen production technology within the energy sector stands as a vital avenue towards decarbonization in process industries. However, for systems characterized by high penetration of renewable energy, the challenges posed by uncertain parameters impacting stable production present significant obstacles to the large-scale application of hydrogen production from renewable energy. To address the issues, this work proposed a comprehensive four-step strategy for the bottleneck identification and optimization of the renewable hydrogen production systems (RHPS), which encompasses four models including system configuration optimization, flexibility analysis, bottleneck identification, and debottlenecking models. The effectiveness of the proposed strategy is substantiated through a case study. The results indicate that positive fluctuations on supply side and negative fluctuations on demand side contribute to bolstering the flexibility of RHPS. When the RHPS possesses sufficient flexibility, the critical fluctuation amplitude for photovoltaic power is −36 %, whereas for wind turbine power, it stands at −32 %. It highlights that when the photovoltaic power fluctuates, the ability of RHPS to maintain its own stability is stronger than when the wind turbine power fluctuates. Furthermore, when the fluctuations of supply side are positive, the rate of change in critical fluctuation amplitudes on the supply side is 4.63 times than that of the demand side. This underscores that the power decline in the supply side has a more pronounced detrimental effect on the stability of RHPS than an increase in the demand side. The proposed strategy for optimizing RHPS offers valuable theoretical insights for the system design and retrofitting under uncertainty.

Thermal management and power generation characteristics in a bifurcating channel by using a piezo-embedded inclined elastic fin assembly during turbulent forced convection of ternary nanofluid

Numerous engineering systems use piezoelectric energy harvesters (PE-EHs), which have several benefits including low cost, simplicity, better power density, and ease of installation. They can be used in different applications for energy harvesting and different external sources such as wind and vortex induced vibrations due to fluid–structure interaction can be utilized. This study uses a unique elastic fin PE/EH assembly for power generation, thermal management, and flow control in a bifurcating channel. Performance of the system is improved by using ternary nanofluid in the channel cooling system. Galerkin weighted residual FEM with ALE is used as the solution method. The convective heat transfer performance and power generation by the EH device are investigated in relation to the varying following parameters: Reynolds number (Re between 10000 and 30000), PE-EH inclination (γ between 0 and 60), fin horizontal location (xf between −H and H), and nanoparticle loading in the base fluid (ϕ between 0 and 0.03). The vortex size and distribution near the junction are significantly influenced by the fin inclination and horizontal location of the fin assembly within the bifurcating channel. When varying other parameters of interest, using nanofluid at the highest loading results in significant deflection of the fin assembly and power generation within the PE-EH. Enhancement factors (EFs) for power generation becomes 15.6 and 39.8 when cases of lowest and highest Re are compared with pure fluid and nanofluid while they are 2.93 and 3.38 for thermal performance improvements. Fin inclination of γ=30 is found as the optimum inclination for achieving the highest power from the assembly. Higher inclination of elastic fin/PE-EH assembly results in cooling performance deterioration while average Nu reduces by a factor of 2.94 by varying inclination from γ=30 to γ=60 with nanofluid. For power generation, effects of using nanofluid with varying inclination is significant and EF becomes 252 from γ=0 to γ=30. There are opposing tendencies for the device’s power generation and cooling performance enhancement when the fin’s horizontal placement is changed. EF for average Nu is 7.25 for varying fin location. As the loading of nanoparticle inside the base fluid increases, the average Nu and generated power exhibit non-linear rising characteristics. Polynomial type correlations are provided for the average Nu and power from the device by varying elastic fin assembly inclination and nanoparticle solid volume fraction. Potential of using hybrid nanofluid in PE-EH embedded thermo-fluid system is shown. The design, development, and optimization of self-sufficient power production systems that may be used to various thermo-fluid systems, such as electronic cooling and thermal control in a range of heat transfer devices, may benefit from the findings.

The vision of the circular factory for the perpetual innovative product

Abstract The growing scarcity of global resources demands a transition from linear to circular production patterns. This article presents a novel vision for integrating linear and circular production processes within a flexible and autonomous production system to achieve perpetual product use. The approach aims to preserve the added value of products and to integrate the design of product generations and production systems. Within the circular factory, the following core aspects must be examined: predicting functions of products, managing uncertainty in used products and process sequences, learning human action for complex tasks, implementing changeable, autonomous production systems, and enabling knowledge modeling for the circular factory across domains. Aspired results are a design for circular factory, effective strategies for uncertainty management and autonomous systems adaptation as well as the externalization of operational knowledge. This research is part of the Collaborative Research Center (CRC) 1574, which explores these aspects in detail. For an in-depth understanding of specific components, it is referred to other publications by the CRC 1574.

Optimising production efficiency: Managing flexibility in Industry 4.0 systems via simulation

The digital transformation, propelled by Industry 4.0, is essential for businesses to adapt to evolving market demands and sustainability pressures. This study addresses the need for improved plant optimisation by enhancing machinery and workstations through advanced digital modelling. Using the Tecnomatix Plant Simulation environment, we developed a simulation platform to assess the feasibility of a flexible production system. The model, based on a validated case study, demonstrated a 15% improvement in system performance by incorporating varying levels of machine flexibility. This improvement highlights the importance of designing production systems that can adapt to changing requirements efficiently. The study shows how increased flexibility in manufacturing environments can enhance operational efficiency and machine utilisation across different scenarios.

Production Prediction and Influencing Factors Analysis of Horizontal Well Plunger Gas Lift Based on Interpretable Machine Learning

With the development of unconventional natural gas resources, plunger gas lift technology has gained widespread application. Accurately predicting gas production from unconventional gas reservoirs is a crucial step in evaluating the effectiveness of plunger gas lift technology and optimizing its design. However, most existing prediction methods are mechanism-driven, incorporating numerous assumptions and simplifications that make it challenging to fully capture the complex physical processes involved in plunger gas lift technology, ultimately leading to significant errors in capacity prediction. Furthermore, engineering design factors and production system factors associated with plunger gas lift technology can contribute to substantial deviations in gas production forecasts. This study employs three powerful regression algorithms, XGBoost, Random Forest, and SVR, to predict gas production in plunger gas lift wells. This method comprehensively leverages various types of data, including collected engineering design, production system, and production data, directly extracting the underlying patterns within the data through machine learning algorithms to establish a prediction model for gas production in plunger gas lift wells. Among these, the XGBoost algorithm stands out due to its robustness and numerous advantages, such as high accuracy, ability to effectively handle outliers, and reduced risk of overfitting. The results indicate that the XGBoost algorithm exhibits impressive performance, achieving an R2 (coefficient of determination) value of 0.87 for six-fold cross-validation and 0.85 for the test set. Furthermore, to address the “black box” problem (the inability to know the internal working structure and workings of the model and to directly understand the decision-making process), which is commonly associated with conventional machine learning models, the SHAP (Shapley additive explanations) method was utilized to globally and locally interpret the established machine learning model, analyze the main factors (such as starting time of wells, gas–liquid ratio, catcher well inclination angle, etc.) influencing gas production, and enhance the credibility and transparency of the model. Taking plunger gas lift wells in southwest China as an example, the effectiveness and practicality of this method are demonstrated, providing reliable data support for shale gas production prediction, and offering valuable guidance for actual on-site production.

Kinetic analysis of solid fuel combustion in chemical looping for clean energy conversion

Chemical looping combustion (CLC) offers an advanced, eco-friendly method for converting solid fuels into energy with inherent CO2 capture, presenting a cost-efficient solution. The kinetics of solid fuel CLC, crucial for reactor design, are not well-understood, limiting its application and optimisation. Conducting a comprehensive kinetic analysis of solid fuel CLC is crucial, as it examines the combustion of both volatiles and fixed carbon, thereby addressing existing knowledge gaps and advancing clean conversion of solid fuels. In this study, a comprehensive kinetic analysis method for the first time has been applied to investigate the kinetics of CLC of low-volatile semi-anthracite coal with CuO at temperatures of 700–950 °C under varied oxygen excess ratios (0.5, 1.0, and 2.0). The results show that the combustion of coal in CLC can be divided into three distinct stages. The combustion of coal with CuO initiates with the combustion of volatiles interacting with solid CuO at 450–580 °C, where the combustion efficiency varies between 3–6 wt%. This is followed by a complex simultaneous mechanism involving gas-phase volatiles and solid-phase CuO, as well as gas-phase oxygen and solid-phase fixed carbon under non-isothermal conditions. The combustion efficiency is ranged 19–71 wt% at the temperature ranges from 580 °C to the isothermal temperatures of 750–950 °C. The activation energy for the combustion of volatiles was determined as Ea = 119 kJ/mol, whereas the initial combustion of fixed carbon in the non-isothermal stage ranged between Ea = 39–53 kJ/mol. The rate of combustion is initially limited by the oxygen diffusion rate from CuO, but with additional oxygen carriers and increased temperature, the reaction becomes constrained by first-order kinetics. Upon reaching the isothermal stage, the final combustion phase between fixed carbon and gas phase oxygen occurs, and the combustion efficiency increases to 77–96 wt% at 750–950 °C. The activation energy for fixed carbon combustion under the isotherm stage was approximately Ea = 234 kJ/mol, with a reaction rate constant (k0) of 7.84 × 109 min−1. This pioneering study not only clarifies the multi-stage kinetics of solid fuel CLC, bridging significant gaps in current knowledge but also sets the foundation for the enhanced design and efficiency of CLC systems for cleaner energy production.

A MODEL FOR FORECASTING DEW POINT PRESSURE IN NIGER DELTA CONDENSATE RESERVOIRS USING CONSTANT VOLUME DEPLETION TESTS DATA

The dew point pressure plays a critical role in developing gas condensate reservoirs. It is a crucial factor used in fluid characterisation, performance estimation of gas reservoirs, and design of production systems. However, the composition of gas condensate fluid varies from one location to another, and thus, empirical correlations have been developed to determine the dew point pressure without conducting routine tests. As a result, correlations that are solely useful in the region where they were developed and analysed are developed. This work developed an empirical correlation using data from gas condensate wells in the Niger Delta using multiple linear regression. The model was developed using the Analytical Tools and techniques in Microsoft Excel. To develop the model, we utilised 63 data sets from the Constant Volume Depletion (CVD) experiment, which involved gas condensates from resources in the Niger Delta. The model comprises an empirical correlation that estimates the dew point pressure for gas condensate reservoirs. It uses compositional information of the fluid and reservoir temperature as input parameters. The correlation is derived through multiple regression analyses. Comparing the prediction accuracy of formulas based on the developed model and other conventional methods indicated that this model is more accurate than other statistical methods in predicting DPP within the Niger Delta region. This model can produce more accurate results than other estimation methods when working under limited field information and time constraints. The correlation developed in this process has a coefficient of determination of R2=0.869 and an Average Absolute Relative Error (AARE) of -2.0767%, along with a root mean square of 195279, indicating a high level of reliability in the proposed correlation.

Optimal Design and Operation of Electrolytic Hydrogen Production and Storage System for Solar Photovoltaic Power Smoothing

Even though the increasing penetration of solar photovoltaic (PV) energy into the electric grid can reduce the carbon emission of power generation, the intermittency and variability of renewable solar energy lead to frequent and steep ramping operations of conventional fossil fuel power plants. Hydrogen production via water electrolysis using solar power can serve as a controllable load and provide a short/long duration energy storage to mitigate the grid fluctuations (i.e., PV smoothing) and improve the resiliency of power grid. The generated green hydrogen will be further stored under high pressure for subsequent utilizations (e.g., hydrogen fuel cell electric vehicle refueling).Polymer electrolyte membrane (PEM) electrolyzer with high efficiency and quick dynamic response is used to generate hydrogen [1,2]. The capacity factors of PV field and PEM electrolyzer can affect the performance of PV smoothing on the cloudy day. Even though PEM electrolyzers directly producing high pressure hydrogen are more energy efficient than ambient pressure electrolyzers followed by mechanical compression, higher pressure leads to more undesired hydrogen back diffusion to the oxygen side, which causes lower hydrogen production efficiency, cell degradation, and safety risk of hydrogen explosion limit [3,4]. Optimal design and operation of electrolyzer under fluctuating solar power are formulated based on an integrated hydrogen-based energy storage system (renewable solar coupled with green hydrogen production and storage). Different PV smoothing scenarios as well as optimal size and operation conditions of the PEM electrolyzer are investigated.The high-fidelity dynamic model of a PEM electrolyzer cell/stack is established with consideration of two-dimensional (“through-plane” and “in-plane”) mass/heat transfer coupled with electrochemical kinetics. The electrochemical reactions are considered using Butler-Volmer kinetics with varying surface molar concentrations of components. Maxwell-Stefan diffusion equation is adopted to calculate the gas-phase species molar concentration in the backing and catalyst layers. Hydrogen permeation back through membrane is included with consideration of both diffusion and convective transports as a function of operational pressure.To evaluate the transient response of the PEM electrolyzer stack, real-time PV data based on an Orlando Utilities Commission (OUC) solar farm is smoothed using the developed power signal control algorithm. The optimal PV smoothing control algorithm and the electrochemical dynamic stack model show the effectiveness of hydrogen-based energy storage system in smoothing the PV signal to improve the grid stability and flexibility.[1] Kumar, S.S. and Himabindu, V., 2019. Hydrogen production by PEM water electrolysis–A review. Materials Science for Energy Technologies, 2(3), pp.442-454.[2] Götz, M., Lefebvre, J., Mörs, F., Koch, A.M., Graf, F., Bajohr, S., Reimert, R. and Kolb, T., 2016. Renewable Power-to-Gas: A technological and economic review. Renewable energy, 85, pp.1371-1390.[3] Trinke, P., Bensmann, B., Reichstein, S., Hanke-Rauschenbach, R. and Sundmacher, K., 2016. Hydrogen permeation in PEM electrolyzer cells operated at asymmetric pressure conditions. Journal of The Electrochemical Society, 163(11), p.F3164.[4] Scheepers, F., Stähler, M., Stähler, A., Rauls, E., Müller, M., Carmo, M. and Lehnert, W., 2020. Improving the efficiency of PEM electrolyzers through membrane-specific pressure optimization. Energies, 13(3), p.612.

Virtual Reality Application for the Safety Improvement of Intralogistics Systems

Immersive technologies from the spectrum of Industry 4.0, such as Virtual Reality (VR), are increasingly used in research and safety analysis in industrial and intralogistics systems, including distribution warehouses and production plants. Safety in intralogistics systems is influenced by design and management processes, human behavior, and device performance. In all these areas, VR can serve as a supportive technology for visualization, testing, and employee training. However, this requires the development of principles for integrating VR into standard procedures for the design, modernization, and analysis of intralogistics and production systems. This article discusses the use of VR to analyze the occupational and functional safety of intralogistics systems. It reviews the literature and VR implementations aimed at examining and improving safety in industrial systems. The article explores the integration of VR into the design and analysis procedures for intralogistics and production systems. The authors present a five-dimensional decision space for assessing the use of VR, including identifying subjects of safety analysis, threats and hazards specific to intralogistics, countermeasures for these threats, factors affecting safety, and mechanisms by which VR can improve safety in intralogistics systems. As a subsequent step, the authors discuss using universal simulation environments that support VR technology to study and enhance safety in intralogistics systems, providing a framework example based on the FlexSim (2023 update 2) environment. Finally, this article addresses the threats and limitations of VR technology, along with the challenges and future prospects of VR in the context of Industry 4.0. The article concludes that VR can be an essential tool for increasing safety in the future, albeit with some reservations about certain features of this technology.

Unlocking the potential of biogas systems for energy production and climate solutions in rural communities

On-site conversion of organic waste into biogas to satisfy consumer energy demand has the potential to realize energy equality and mitigate climate change reliably. However, existing methods ignore either real-time full supply or methane escape when supply and demand are mismatched. Here, we show an improved design of community biogas production and distribution system to overcome these and achieve full co-benefits in developing economies. We take five existing systems as empirical examples. Mechanisms of synergistic adjusting out-of-step biogas flow rates on both the plant-side and user-side are defined to obtain consumption-to-production ratios of close to 1, such that biogas demand of rural inhabitants can be met. Furthermore, carbon mitigation and its viability under universal prevailing climates are illustrated. Coupled with manure management optimization, Chinese national deployment of the proposed system would contribute a 3.77% reduction towards meeting its global 1.5 °C target. Additionally, fulfilling others’ energy demands has considerable decarbonization potential.