The phasing out of high-GWP refrigerants and the growing diversity of working fluids used across heat pumps, refrigeration systems, and closed-cycle power applications have made drop-in fluid replacement a question of significant practical interest. Centrifugal compressors are designed around the thermophysical properties of a specific fluid, and the performance penalty is incurred when working fluid is replaced without redesigning the impeller. This study presents a CFD comparison of direct drop-in fluid replacement in a fixed geometry centrifugal compression system. Eight working fluids that span the property range relevant to current drop-in substitutions are evaluated: air, nitrogen, argon, carbon dioxide, R22, R134a, R1234yf, and R1234ze(E). A reference centrifugal impeller was reconstructed in ANSYS BladeGen, meshed in ANSYS TurboGrid using the Automatic Topology and Meshing method, and simulated in ANSYS CFX (2024 R2) as a single periodic passage with Frozen Rotor interfaces and Spalart–Allmaras turbulence closure. Performance maps were generated for each fluid across a range of rotational speeds and mass flow rates, with a common inlet reference condition applied across all cases to isolate the influence of fluid properties from an inlet state. The resulting dataset enables a like-for-like comparison of pressure ratio, efficiency, and shaft power requirement, providing a basis for assessing the aerodynamic implications of drop-in fluid substitution in centrifugal compression systems. Air, nitrogen, argon and carbon dioxide achieved similar peak efficiencies (~88%) and comparable pressure ratios (PR), indicating they can be used as drop-in substitutes without performance loss. Refrigerants R1234yf and R1234ze(E) matched R134a in efficiency (peak ~90%) while offering higher pressure ratios and significantly lower power requirements at peak efficiency. At 20,000 RPM and a mass flow rate of 2 kg/s, compared to a PR of 1.45 for air, nitrogen, carbon dioxide and argon achieved PRs of 1.4, 1.9 and 2.4, respectively. At the same settings, R134a and R1234 refrigerants reached PRs of 5 and 6, respectively. The power requirement was ~8 × 104 W for air and similar fluids, and ~11 × 104 W for refrigerants.

In recent years, packed-bed systems have emerged as an attractive design for thermal energy storage systems due to their high thermal efficiency and economic feasibility. As integral components of numerous large-scale applications systems, packed-bed thermal energy stores can be successfully paired with renewable energy and waste heat to improve energy efficiency. An analysis of the thermal performances of two packed beds (hot and cold) during six-hour charging and discharging cycles has been conducted in this paper using COMSOL Multiphysics software, utilizing the optimal design parameters that have been determined in previous studies, including porosity (0.2), particle diameters (4 mm) for porous media, air as a heat transfer fluid, magnesia as a storage medium, mass flow rate (13.7 kg/s), and aspect ratio (1). The performance has been evaluated during both the charging and discharging cycles, in terms of the system’s capacity factor, the energy stored, and the thermal power, in order to understand the system’s performance and draw operational recommendations. Based on the results, operating the hot/cold storage in the range of 20–80% of the full charge was found to be a suitable range for the packed-bed system, ensuring that the charging/discharging power remains within 80% of the maximum.

Model validation is an essential part of CFD-based projects. Despite being successfully employed for decades, the level and extent of CFD model validation details vary significantly in the published literature, which, in turn, adversely affects the repeatability and usefulness of published models and data. This study explores the various challenges associated with validating CFD models of thermodynamic components, namely, the compressors and their performance evaluation. The methodology involves blade generation through TurboGrid and BladeGen, mesh generation to ensure computational efficiency, and pre-processing with CFX to define boundary conditions and turbulence models, all within ANSYS 2024 R1. Three case studies are discussed, each assessing different compressor configurations and common challenges encountered during the model validation stage. Based on the case studies, a number of recommendations are presented relating to best practices in terms of both the use of published materials to validate new models and the level of detail required for experimental or simulation publication to ensure they can be replicated or used to validate a new model.

The volatility of renewable energy outputs is a well-known obstacle that has hindered the integration of more renewables in the UK’s energy mix, as the current network was not designed to handle such swings. Microgrids (MGs) may function as an effective means of integrating more renewables, particularly if they can effectively control the volatility of renewables at a smaller scale (the MG level) through a collaborative operational strategy. This paper focuses on the management of renewable energy fluctuations in MGs, proposing a pre-contract order update (COU) strategy based on the lean balancing (Heijunka) concept. The study compares the performance of collaborative and selfish MGs in terms of levelized cost of electricity (LCOE), order volatility, and carbon emissions. Two simulations models for the collaborative and selfish MGs were implemented, while considering two distinct backup generation scenarios within the MG system. The findings indicate a two-dimensional trade-off between the collaborative MG models, which are 61% more sustainable and reduce order volatility to the utility grid by 55%, and the selfish MGs, which incur lower energy consumption costs reduced by only 19%. These findings highlight the potential of collaborative MGs in enhancing grid stability and supporting broader renewable energy integration goals.

The increasing demand for hydrogen has made it a promising alternative for decarbonizing industries and reducing CO2 emissions. Although mainly produced through the gray pathway, the integration of carbon capture and storage (CCS) reduces the CO2 emissions. This study presents a sustainability method that uses flare gas for hydrogen production through steam methane reforming (SMR) with CCS, supported by a techno-economic analysis. Data Envelopment Analysis (DEA) was used to evaluate the oil company’s efficiency, and inverse DEA/sensitivity analysis identified maximum flare gas reduction, which was modeled in Aspen HYSYS V14. Subsequently, an economic evaluation was performed to determine the levelized cost of hydrogen (LCOH) and the cost–benefit ratio (CBR) for Nigeria. The CBR results were 2.15 (payback of 4.11 years with carbon credit) and 1.96 (payback of 4.55 years without carbon credit), indicating strong economic feasibility. These findings promote a practical approach for waste reduction, aiding Nigeria’s transition to a circular, low-carbon economy, and demonstrate a positive relationship between lean and green strategies in the petroleum sector.

The petroleum industry has been a key driver in the development of the world economy yet continues to attract increasing criticism due to its negative environmental impact via greenhouse gas emissions, resource inefficiency, and waste. Integrating lean and green practices is fast becoming a transformative approach to tackling these issues as it integrates process optimization with sustainability principles. This review paper examined the existing literature on lean and green integration, highlighting its benefits, models, critical success factors, and a roadmap for its implementation. Also, it identified sustainability challenges and offered strategic solutions. The findings showed that integrating lean and green offers potential for both process optimization and waste and carbon footprint reduction, particularly for the petroleum industry in Sub-Saharan Africa (SSA). This can be achieved by using appropriate tools and models. Sustainable value stream mapping (Sus-VSM) is a strategic tool that highlights the importance of sustainability metrics. These sustainability metrics address the implementation challenges of the convectional value stream mapping tool (VSM). This paper is one of the few initiatives to promote lean–green integration within the petroleum industry.

In recent years, packed-bed systems for large-scale applications have emerged as a highly promising design for Thermal Energy Storage systems because of their high thermal efficiency and economic feasibility. Large-scale application systems typically include packed-bed thermal energy stores as essential components, enabling effective integration with renewable energy and processed heat. The packed-bed systems investigated in this paper utilise Magnesia as the storage medium and optimised parameters, which have previously been identified through research involving charging and discharging cycles of both the hot and cold storage systems when air is the heat transfer fluid. This includes solid particle diameters of 0.004 m, a material porosity of 0.2, an aspect ratio of 1 for the storage tank, and a mass flow rate of 13.7 kg/m3. This paper aims to present a comparative analysis of the influence of alternative heat transfer gases, namely air, argon, carbon dioxide, helium, hydrogen, and nitrogen, on the performance of Pumped Thermal Energy Storage hot and cold storage systems. The performance of the six gases in the storage system was evaluated using an axisymmetric model simulated with COMSOL Multiphysics 5.6 software, with the total energy stored and the capacity factor serving as key performance indicators. The results revealed that carbon dioxide gas was the most promising heat transfer fluid and that the packed bed could be operated efficiently over 72% and 76% of its range for hot and cold systems, respectively. Hydrogen, nitrogen, and air performed similarly but less adequately than carbon dioxide and had operating ranges of 55% and 75% for hot and cold storage. Helium and argon had the poorest performance, with optimal charging and discharging rates corresponding to 50% and 66%.

This paper explores the integration of microgrids within utility networks and distinguishes selfish from collaborative microgrids. Research has shown that selfish microgrids tend to increase volatility of order updates to power generators, whereas collaborative microgrids decrease that volatility, resulting in smoother, more controllable operations of networks. This paper proposes an analytical formula linking power volatility to power quality, i.e., to issues such as voltage dips, surges, and transients. These are known risks for disrupting the operation of utility grids, causing instability and jeopardising efficiency and reliability. As collaborative microgrids reduce volatility, they improve power quality. That argument is extended to propose that collaborative microgrids can act as quality improvements agents within wider networks.

The United Kingdom seeks to achieve net-zero emissions by 2050, mostly via the shift to an electrical system exclusively powered by zero-carbon sources. Microgrids (MGs) can be seen as an effective system for integrating renewables into the energy portfolio. Nonetheless, MGs face the acknowledged obstacle of backup power generation due to the intermittent nature of renewable energy sources, necessitating the establishment of backup power generation capacity. This paper contrasts selfish power generation, where the MG pursues complete energy autonomy, with an alternative influenced by lean principles (Heijunka production), which seeks to stabilise power transactions within the national electricity supply chain, reduce emissions, and tackle the backup generation challenge. This study proposes a pre-contractual order update (COU) strategy for the operation of hybrid collaborative MG where a forward order update to the utility grid is placed, in contrast to selfish MG, which uses a spot order update strategy. The COU strategy was defined, and two simulation models (for selfish and collaborative MG) were developed, each incorporating four backup generation scenarios to illustrate the method’s efficacy by assessing the system’s critical performance metrics. It has been found that the collaborative MG model reduced the carbon emissions by 62% and the volatility of unplanned orders to the grid by 61% compared to the selfish model in the first scenario (grid-dependent MG). Furthermore, the MG achieved zero volatility and a 33% reduction in carbon content in the collaborative MG when using the H2 burner as backup generation compared to the first scenario. Indicating that sustainability encompasses not only the use of renewable resources but also the stability of their outputs through the implementation of collaborative MGs.

Microgrids (MGs) have emerged as viable alternatives for delivering electricity to remote rural regions in a secure and ecologically sustainable manner. However, utilizing microgrids in a more collaborative manner might greatly enhance the integration of renewable energy sources into the electricity network. The primary objective of this research is to improve the quality of information communicated from MGs to the utility grid to achieve production levelling. Multiple simulation scenarios have been developed to analyse the performance of a grid-connected MG when the grid order update rules are altered. The findings indicate that the suggested scenarios for collaborative MGs, especially the scenario that relies on forecasted demand for precontracted order updates (COU), are enhancing system performance by stabilizing order volatility to the grid (58% less unplanned volatility of orders in the collaborative MG), thereby reducing the carbon footprint of the MG by 67% and increasing storage utilization by 74%. The only limitation was the volume of exported electricity; however, the implementation of long- term storage capacity (seasonal storage) has effectively reduced the exported power to 0. A distinct trade-off exists between enhanced storage capacity at a higher cost and a significant volume of exported power. The optimal resolution for this trade-off is greatly affected by the initial investment in storage technology and the feed-in tariff rate for exported power. The main conclusion may be summarized as the transition towards more collaborative MGs may serve as the foundation for developing more decentralized electrical networks and integrating more renewable energy sources into the current electricity system.

The International Energy Agency states that geothermal energy technologies could meet 15% of the global electricity demand growth, provided cost reductions continue. Organic Rankine Cycle (ORC) systems are expected to play a key role in achieving this ambitious target. Recognized for their effectiveness in converting low-to-moderate temperature heat, ORC systems are already in use in numerous installations. The performance of ORC systems is primarily influenced by operational conditions and the choice of working fluid. A key system design challenge arises from the operational conditions of ORC systems, which are closely tied to the design and sizing of heat exchange components. This study examines the effect of the pinch point temperature difference, and the approach point temperature on the thermodynamic performance of a low-temperature ORC, with cycle efficiency and the total heat transfer area of the evaporator serving as the main performance indicators. The analysis uses a parametric approach to assess ORC performance by varying pinch point and approach point temperatures for a range of suitable working fluids. An optimal design region is identified, where the trade-off between thermal efficiency and heat exchanger size is most advantageous. These results offer valuable theoretical insights for low-temperature ORC design, highlighting the importance of selecting pinch point and approach point temperatures that strike a balance between thermal and economic goals.

Substantial breakthroughs in renewable energy have been made in order to reduce energy-induced climate change. Yet our reliance on these sources is still insufficient. The UK’s objective of attaining net-zero emissions by 2050 is highly dependent on shifting to an electrical system that exclusively relies on zero-carbon generation. This entails integrating renewable energy sources, along with other low-carbon sources such as nuclear power, into the energy mix. However, the primary barrier to incorporating additional renewable energy sources into the grid is their intermittent and volatile nature. Therefore, there is a pressing need to stabilise the generation of renewables and manage this volatility by enhancing the balancing mechanism between microgrids and the national grid. This paper examines previous research on microgrids and smart grids, specifically from a supply chain perspective. It has been observed that the majority of the current literature focuses on documenting selfish microgrids that strive to optimise performance at the microgrid level. However, there is an alternative approach that draws inspiration from the field of supply chain management. Consequently, it is possible to enhance a microgrid’s performance within the broader system that it belongs to by reconsidering the timing and location of storage utilisation.

The ever-growing energy demand across the globe puts significant pressure on the power generation sector to find renewable and sustainable alternatives to fossil fuels. Organic Rankine Cycle (ORC) technology can be applied to low-medium temperature heat sources to generate useful work. In this paper, an ORC cycle, powered by a medium temperature exhaust gas at 423.15 K, is modelled. Working fluids R1234ze and R1234yf were compared as replacements to R134a and considered across a variety of operating conditions comparing both the energetic and exergetic performance. Sizing of heat exchangers (evaporator and condenser) has also been undertaken. Following a comprehensive evaluation, R1234ze is recommended as a viable replacement to R134a, specifically regarding the pump and turbine performance.

The use of thermal energy storage (TES) contributes to the ongoing process of integrating various types of energy resources in order to achieve cleaner, more flexible, and more sustainable energy use. Numerical modelling of hot storage packed bed storage systems has been conducted in this paper in order to investigate the optimum design of the hot storage system. In this paper, the effect of varying design parameters, including the diameter of the packed bed, the storage material, the void fraction, and the aspect ratio of the packed bed, on storage performance was investigated. COMSOL Multiphysics 5.6 software has been used to design, simulate, and validate an axisymmetric model, which was then applied to evaluate the performance of the storage system based on the total energy stored, the heat transfer efficiency, and the capacity factor. In this paper, a novel-packed bed was proposed based on the parametric analysis. This involved a 0.2 void fraction, 4 mm porous media particle diameter, and Magnesia as the optimum storage material with air as the heat transfer fluid.

This article presents an analytical solution for the evaluation of the thermal performance of packed bed sensible heat storage. The numerical model developed was tested for four different solid storage mediums. The thermal energy equation is solved numerically by deploying the finite difference method. The presented analytical solution is based on a novel mathematical approach. The numerical model was validated using the computer simulation package Comsol Multiphysics v5.3. Our numerical model results are in good agreement with the published experimental data, with an overall difference of ~10%. Hence, the numerical model is an efficient way of evaluating the thermal performance of packed bed thermal energy storage systems compared to other numerical strategies or computer simulation techniques. This proves that the novel analytical model has shown to be a reliable and broadly accurate approach to acquire the thermal performance of sensible heat storage.

This chapter gives an overview of the concentrating photovoltaics technology which combines an optical device with a solar cell to concentrate the sun beams in order to increase the electrical output. The various types of optical devices, solar cells and tracking systems that are suitable for this technology have been discussed. Since the concentration of the sun beams can raise the cell temperature significantly, the cooling of the cell using heat sinks has also been explained. Finally, the chapter discusses the strengths and weaknesses of the technology.

Energy storage is crucial for solar energy utilisation. This chapter provides an introduction into different energy storage types and focuses on batteries, their operation and applications, battery technologies, characteristics and management.

The literature presents a review of the current application of lean and green practices and highlights the context, barriers, drivers, tools, and critical success factors and tools for the integration of lean and green practices within the Sub-Saharan African (SSA) manufacturing industry. In addition, the current state, gaps in the existing literature, and future perspectives on the current challenges of the industry have been suggested for the integration of both paradigm and sustainability of the industry. Integration of lean-green provides means to addressing issues faced by manufacturing industries for improved competitiveness. This study demonstrates that the application of lean and green practice is critical to be identified and applied within the region through the analysis of research papers published in both operational and sustainability journals. The findings of this review indicate that the synergy of lean and green practices is an effective initiative for resolving the operational and sustainability challenges of the manufacturing industry within SSA. To achieve operational excellence and mitigate environmental concerns, industries must implement the right tools and consider the critical success factors. By applying appropriate tools and models, the SSA region can address the current challenges and achieve a reduction in cost significantly.

In recent years, there has been an increase in the use of renewable energy resources, which has led to the need for large-scale Energy Storage units in the electric grid. Currently, Compressed Air Energy Storage (CAES) and Pumped Hydro Storage (PHES) are the main commercially available large-scale energy storage technologies. However, these technologies are restricted geographically and can require fossil fuel streams to heat the air. Thus, there is a need to develop novel large-scale energy storage technologies that do not suffer from the abovementioned drawbacks. Among the in-development, large-scale Energy Storage Technologies, Pumped Thermal Electricity Storage (PTES), or Pumped Heat Energy Storage, stands out as the most promising due to its long cycle life, lack of geographical limitations, the absence of fossil fuel streams, and the possibility of integrating it with conventional fossil-fuel power plants. There have been a number of PTES systems proposed using different thermodynamic cycles, including the Brayton cycle, the Rankine cycle, and the transcritical Rankine cycle. The purpose of this paper is to provide a comprehensive overview of PTES concepts, as well as the common thermodynamic cycles they implement, indicating their individual strengths and weaknesses. Furthermore, the paper provides a comprehensive reference for planning and integrating various types of PTES into energy systems.