IGEC Transactions, Volume 1: Energy Conversion and Management

Floating photovoltaic (FPV) system offers advantages, such as being free from shading and large open land area, controlling water losses and algae boom, minimising dust pollution, being easy to maintain, and lowering temperature due to water evaporation. In addition, the low operating temperature of solar cells will increase the system's electrical energy output and efficiency. This research will investigate the potential of a natural convection cooling loop to decrease the temperature of FPV panels without external energy. The objective is to develop a numerical model for the entire system, which includes radiation absorption, natural convection, heat conduction and electrical power generation, to understand and optimise the thermal performance. This is achieved by first modelling a simplified natural convection cooling loop, using computational fluid dynamics and then by gradually adding further modelling elements, to take into account the daily variation of heat input, thermal radiation exchanges, heat conduction, electrical generation, and heat losses. Preliminary results show that the natural convection cooling loop system effectively improves the cooling rate of FPVs. Simulations produced so far, provide important and new insights of how natural convection cooling can be introduced to FPV cells and how it can be optimised.

The widespread use of lithium-ion batteries (LIBs) in portable electronics such as smartphones and laptops highlight the significant need for long-lasting battery availability. The rise of electric vehicles (EVs) has further increased LIBs demand, leading to concerns about the depletion of lithium sources from the earth. Extracting methods such as pyrometallurgy, hydrometallurgy, and direct methods have been introduced to recover lithium. However, these methods most often result in environmental pollution. Therefore, it is imperative for a ‘greener’ or environmentally friendly method to be established. The introduction of an electrochemical method to recycle LIBs in 2016 is a big step towards fulfilling this goal. This technical paper comprehensively examines the various techniques for recycling lithium from spent LIBs. The focus is on evaluating and discussing the extent of usage, technological readiness, efficiency, and environmental aspects of the methods. Also, the techno economy aspect of generic LIBs recycling methods has been reviewed and included.

Lithium-ion batteries (LIBs) have been widely used in various applications including portable devices, electric vehicles, and large-scale energy storage systems. Compared to other battery types, LIBs have a high power density, relatively higher cycle life, long run time, good discharge or charge cycles, and low cost. The use of LIBs aligns with a global-wide strategy in pursuing sustainable development and a circular economy as it helps decarbonize the transportation and power sectors. As such, Li supply has a heavy burden, and the demand will consistently hike soon. Therefore, re-circulating spent LIBs from industries or consumers’ waste is a promising strategy. To secure a closed loop of Li, consumers need to embrace positive environmental stewardship by returning used e-waste to the recycling entry points. Then, the non-renewable resources would be passed over to a number of stakeholders and go through a sequence of recycling processes before re-entering the usage loop. In pursuit of tracking the lifecycle of spent Lithium-ion Batteries (LiBs), this article undertakes an initial examination of electronic waste (e-waste) resource dynamics within the context of Malaysia. Through document analysis of data from sixty-three e-waste recycling stakeholders’ websites, the study has categorized the phases of e-waste flows, based on the recycling processes. According to the data, a noteworthy proportion of recycling stakeholders are involved in the preliminary phase of the ‘resource entry point’, whereas a comparatively small fraction of them engages in the subsequent phase of ‘resource segregation’. Conversely, a meager number of recyclers are involved in ‘resource extraction’, particularly concerning the ‘retrieve’ activities. Notably, data on detailed activities within each phase is fragmented and there is currently no evidence found on active LiBs recycling activities in Malaysia. With strategic effort, there are achievable potentials for efficient e-waste circular flows in Malaysia, where valuable resources like Li can be extracted. Recycling interventions, infrastructure, and legislation are among the integral aspects to be researched and designed, to optimize e-waste circularity in Malaysia.

The thermoacoustic refrigerator (TAR) is a device that converts acoustic energy into thermal energy to generate a cooling effect. This paper establishes a simplified theoretical model of a loudspeaker-driven standing-wave TAR based on the linear thermoacoustic theory. A parametric analysis is carried out to study the effect of the boundary conditions on the performance of the TAR. The result shows that variation in the acoustic impedance at the end of the resonance tube affects the acoustic characteristics and the cooling effect of the system and, thus, the selection of the optimal driving frequency of the loudspeaker. This work provides guidelines for designing TARs to meet specific requirements of thermoacoustic refrigeration.

In this contribution, we make an attempt to write a theoretical proposal for designing an eco friendly thermal power plant which runs with cold nuclear fusion technology at a temperature of 1500–2000 °C. In our recently published papers, we have proposed a clear cut mechanism for understanding and implementing cold nuclear fusion technique pertaining to fusion of hydrogen with metals of mass numbers starting from 50. In this context, we would like to stress the point that, fusion of hydrogen under controllable temperature and pressure can be understood as a phenomenon of fusing neutron to the nucleus of the base atom. Part of isotopic nuclear binding energy difference of final and base atomic nuclides can be seen in the form of safe thermal energy of the order of (1–3) MeV per atom against 200 MeV released in nuclear fission of one Uranium atom. Due to increased heaviness and weak interaction, sometimes fused neutron splits into proton and electron. Proton seems to be retained by the base atom’s nuclear core and electron seems to join with the electronic orbits of the base atom. In this way, increased mass of base atomic nuclide helps in eco friendly production of thermal energy in large quantity. For this purpose we consider Iron-56 as a fuel. In a simplified view, under strong nuclear attractive forces, Iron-56 absorbs hydrogen atom as a neutron and by emitting 1 MeV equivalent thermal energy transforms to Iron-57. Thus, one gram of Iron-56 can generate 1000 MJ of heat with 50% efficiency. In a shortcut approach, by bombarding powder and semi-liquid forms of Iron-56 with direct neutrons coming from neutron source, our proposal can be tried, understood and verified experimentally.

Ceramic electrolytes play a crucial role in the development of ceramic electrochemical cells. In this study, we explore the conductivity behavior of two notable ceramic electrolytes, oxide ion conducting Ce0.8Sm0.2O2 (SDC) and proton conducting BaCe0.7Zr0.1Y0.1Yb0.1O3-δ (BCZYYb), under varying ambient conditions. Using the DC four-probe method, we examined the conductivity of these electrolytes in the temperature range of 850–450 °C under pure air, 3% H2O-air, and 5% H2O-air conditions. The results reveal that SDC has a higher total conductivity, mainly oxide ions, and electrons, than BCZYYb, which conducts protons more effectively. SDC has a higher electron density and larger crystallite size than BCZYYb, which could account for its higher bulk conductivity. BCZYYb exhibits effective proton conductivity at lower temperatures (below 750 °C) through hydration with lower activation energies. The enthalpy of protonation in BCZYYb reaches −21.38 kJ mol−1 under 3% H2O-air conditions from 450 to 550 °C. The findings of this study provide critical insights into the fundamental mechanisms that govern the conductivity of ceramic electrolytes and can guide future efforts to optimize ceramic electrolytes for advanced electrochemical conversion and energy storage applications.

Despite an increase in electric vehicle chargers (EVCs) connecting to low-voltage (LV) power distribution networks managed by Distribution Network Operators (DNOs) and National Grid ESO [1] expecting approximately 33 million electric vehicles (EVs) on United Kingdom (UK) roads by 2050 under a ‘falling short’ scenario, the optimised location for installing EVCs on an LV DNO network with respect to harmonics has not been fully explored. The purpose of this paper is to investigate the optimal point of coupling for EVCs to minimise the steady-state voltage total harmonic distortion (THDv) on a radial LV DNO network under both normal and steady-state fault conditions. The conclusions will aid DNOs with future network planning and design. Minimising harmonics is important. Balda et al. [8] explains that harmonics can increase the risk of computer errors and cause communication interferences. Whilst Bhattacharyya et al. [9] explains that harmonics increase the risk of motor overheating and reduce the life of transformers, increasing the risk of power cuts. Using MATLAB, a simulation system for a radial 230m LV feeder based upon a suburban residential LV DNO network was created and was divided into eleven sections (busses) with base load connected, each with the capacity for an EVC to be connected to all three phases. Each two-buses were connected with impedance which represents cables. The first and last bus represents the first and last 5% of the network length. The remaining busses each represent 10% of the network length. A broad range of scenarios were generated by changing network parameters. These parameters include: Transformer size at 50, 200, 500, 1000 and 2000 kVA. Mains cable size at 35, 185 and 300 mm2. Service length at 15 and 30 m. X/R ratio and magnitude of 11 kV bus impedance representing three different 11 kV networks. Existing photovoltaic (PV) generation and EVCs connected to the network at 1st, 11th and all busses. A two or three-phase fault at Bus 11 forcing yellow and/or blue phase supplies to be fed via red phase. One or two LV feeders connected to bus 1. Using two algorithms, the optimal bus for EVC point of coupling with respect to lowering the steady-state THDv was identified. The first algorithm is based on the Elephant Herding Optimisation (EHO) technique (Wang et al. [19] and Meena and Yang [18]). The second algorithm for comparison is based on the Monarch Butterfly Optimisation (MBO) technique (Wang et al. [19]). Alterations have been made to these scripts to allow them to solve the optimisation of EVC point of coupling with respect to THDv. It was found that for all scenarios except for the ’50 kVA transformer’ and ‘PV generation connected to all busses’ scenarios, the optimal EVC point of coupling was the 1st bus, representing the first 5% of the network fed from the 11 kV:400 V transformer. This aligns with what would be expected since the shorter the distance current harmonics need to travel, the lower the cable impedance and the lower the magnitude of harmonic voltage drop generated as per Ohms law.

The distributed power system in remote and rural area is the main challenges of the future smart grid development. Compared with traditional gasoline pipelines and gas station construction for fossil fuel vehicles, the high demand of Electric Vehicles (EVs) will then consider breaking their products in the market of remote areas. While the EVs adoption is growing globally, remote areas present unique challenges such as limited charging infrastructure, long transmission distances, and varying energy demands under the EVs penetration. This paper firstly defines the typical Remote Areas where the grid density is low with poor cable properties, but the future power demand is growing fast. Besides, their distributed energy is abundant and can contribute to generating electricity for the EVs, including solar energy, wind energy or marine energy. Renewable energy can maximize the reduction of carbon footprint and consumption. Then the Trincomalee city is selected as the simulation object. Perform national grids simulation optimization and solar power generation management as demand side optimization. Power grid model simulation: use government data to predict that Trincomalee's electricity demand will increase from the current 40–640 MW in five years and build a grid simulation model for Sri Lanka's national high-voltage transmission lines. Optimization will target transmission cables that provide higher voltage loads while considering cable properties includes resistance and reactance for optimal power loss and voltage drop respectively, thermal capacity, and power quality. The results show that though a lower resistance and reactance can reduce the power loss and voltage drop during transmission, the relationship does not follow a linear relationship when integrating into the whole power system. The range of resistance and reactance scenarios is set with equal intervals. The optimal point with the higher drop scenario always exists. The demand side management aims to solve the Trincomalee’s intermittency and overproduction issue to keep the supply and demand in dynamic balance. Solar energy production is surveyed for its intrinsic intermittency between day and night time. Based on the climatic conditions and NCRE data in the Trincomalee in 2013, the PV generation system is simulated by MATLAB and Excel and conclude the curves of solar irradiance, temperature and demand. the optimum rated capacity of the battery is then concluded from the demand and solar curve. The case studies from Sri Land highlight successful examples of distributed grid management in remote and rural areas. The methods contribute to the broader conversation around sustainable transportation and energy systems, and to provide guidance for policymakers and industry stakeholders working towards sustainable and equitable EV adoption.

High-parameter transcritical CO2 power cycle is regarded as one of the most promising energy conversion systems owing to its high efficiency and compactness. However, it is possible to further improve the thermal efficiency and reduce the size of components by adding CO2 binary mixtures. In this study, a comprehensive thermodynamic analysis of the high-parameter transcritical power cycle enabled by CO2–SO2 mixture is conducted. Simulation results indicate that thermal efficiency increases with the SO2 mass fraction, while specific work does the opposite. And thermal efficiency increases abruptly as the SO2 mass fraction is higher than 0.2. However, thermal efficiency decreases with the SO2 mass fraction at higher maximum pressure. Additionally, system at a higher minimum temperature is more sensitive to SO2 mass fraction. This preliminary research will serve as a guideline for the optimization of transcritical power cycle enabled by CO2–SO2 mixture at design and off-design conditions.

According to International Energy Agency, active space cooling and air conditioning systems are essential to maintain indoor thermal comfort, which consumes approximately 16% of the building sector’s final electricity consumption and contributes 3.94% of global greenhouse gas emissions. In this regard, low-cost but effective passive solutions have immense potential to improve operational energy efficiency in the building sector. The use of organic phase change materials (PCM) on building envelope can provide high thermal mass and thus can lower the temperature fluctuation inside the building. Porous biochar has been used as a matrix to compensate for the low leakage stability and low thermal conductivity of available PCM (OM35). The biochar has been obtained through pyrolysis of water hyacinth and co-pyrolysis of water hyacinth (W), sugarcane bagasse (S), and yellow oleander (Y) at a temperature of 550°C, for a holding time of 1 h and at a heating rate of 10°C/min in Argon environment at a fixed bed batch reactor. Two biocomposite PCMs, namely, W-PCM and T0_SWY-PCM, are made through simple impregnation method. The durability and dependability of the developed biocomposite PCMs need to be studied before applying them to building envelopes. In the current study, W-PCM and T0_SWY-PCM are studied for the change of thermal, physical, and chemical properties after performing thermal cycling tests and compared with OM35. The material has been subjected to simultaneous heating and cooling cycles in a developed thermal cycling chamber within a temperature range of 25–45°C. The temperature inside the insulated chamber is maintained with the help of a tubular air heater (500W), eight Peltier cooling elements (12 V, 60 W), and two Subzero temperature controllers (SZ7569). The samples have been tested for leakage, chemical, and thermal stability after the completion of 50,100,150 and 200 thermal cycles, respectively. The FTIR and XRD analyses confirm that no significant changes occur in the functional groups for both the biocomposite PCMs after completing 200 thermal cycles. Also, the leakage stability of the two biocomposite PCMs has been ensured through the leakage stability test on filter paper (Whatman Grad 1, 11 µm size). The DSC results showed that there is a reduction of 0.25% and 2.58% in melting point and heat of fusion, respectively, after 200 cycles for W-PCM. The negligible changes in physical, thermal, and chemical properties over the thermal cycles confirm that W-PCM is a good option to be used as thermal energy storage material in building envelopes.

Hybrid trucks with low fuel consumption, low emissions and long range are considered to be effective fuel-efficient trucks. In addition, the engine of hybrid truck is still one of the main power sources. As a large amount of energy of engine is dissipated in the form of waste heat, waste heat recovery (WHR) system is still a promising energy saving solution. The operating conditions of trucks are complex and varied. In order to evaluate the energy saving effect of hybrid electric vehicle (HEV) coupled with WHR system under different road conditions (urban, suburban and highway), the dynamic model of hybrid truck equipped with WHR system was constructed in this study. The validity of the model was then verified by experimental data. The analysis of the vehicle performance and the energy saving effect of the WHR system was carried out based on the operation results of the dynamic model under different road conditions. The study results show that the effective operation interval of the WHR system is suburban and highway conditions, while the energy saving effect is not significant in urban conditions. When the target truck is fully loaded, the WHR system can save 6.36% fuel under mixed working conditions. The urban conditions are the superior operating range for the hybrid system. The combination of two energy-saving technologies enables efficient use of energy in all operating conditions. The study of the dynamic characteristics of the HEV-WHR system is of great importance for the practical application of both systems.

The versatility in the selection of feedstock for the generation of high quality syngas has emerged dual fluidized bed gasification (DFBG) as a very promising technology. The principle of DFBG is heavily dependent on the hydrodynamics and heat transfer characteristics of the gas–solid system. Furthermore, the complexity of the hydrodynamics increases with more number of fuels of different physico-chemical characteristics due to its nonlinearity and transience. It is, therefore very essential to study the hydrodynamics of the multi-phase system. The present work focuses on two fluid model simulations of a dual fluidized bed gasifier to study the impact of biomass-coal blending with silica sand as the bed material. The simulation has been performed using the Multiphase Flow with Interphase eXchanges (MFiX) simulation platform. Six different biomass-coal (BM:C) blending proportions such as 0:0, 1:5, 2:5, 3:5, 4:5 and 5:5 are considered for the 2-D two fluid model (TFM) simulation of the gasifier. An optimum superficial air velocity of 0.2 m/s is considered for the entire simulation for a grid of 6000 computational cells. The impact of biomass-coal blending proportions on the static pressure and pressure drop, axial and radial voidage, suspension density, radial solid velocity profile and granular temperature are analysed using the Paraview software. The numerical investigation has revealed that the static pressure decreases with an increase in biomass-coal blending proportion for a fixed height within the gasifier. The axial bed voidage has also dropped with a surge in biomass-coal blending proportion up to 0.6 m height from the bottom and then has started to rise until it becomes unity at a height of about 0.9 m. Moreover, suspension density has enhanced with a rise in blending proportion due to the density difference between biomass and coal. The difference in density and particle size of biomass and coal has also contributed towards a decrease in the radial voidage and solid velocity with increased blending proportion. An escalation in granular temperature has been observed with the decline in solid volume fraction for all the blending proportions. However, the granular temperature has dropped with an increase in blending proportion. This numerical study will act as a platform for the experimental investigation of the biomass-coal blending performance of the DFBG system.

Solar energy is clean and abundant, and it has been considered as a green energy. Solar energy can be utilized by photovoltaics (PV) or concentrating solar power (CSP) generation. PV converts solar energy into electricity cost-effectively, but it is sensitive to environmental conditions, such as weather, and unable to utilize spectrum outside the spectral response range. In contrast, CSP is less sensitive to environment conditions, but more costly than PV. Their advantages can be combined by integrating PV and CSP into a beam-splitting photovoltaic-thermal (PV-T) hybrid system, which splits incident solar radiation and projects the photovoltaic spectrum onto solar cells and the rest to CSP. In this way, full spectrum of solar radiation can be utilized, and the overall conversion efficiency is enhanced by increasing the efficiency of solar cells and heat. The beam-splitting PV-T system has shown a great potential in solar energy production; however, few studies consider hybrid systems with the newly emerging perovskite solar cells. This paper reports a perovskite solar cell (PSC) with various bandgaps by replacing Iodine ion (I−) in FA1-xMAxPbI3 with Bromine ion (Br−) in different proportions. In addition, the spectral response ranges of PSC were adjusted to 300–700, 300–730, 300–780, 300–800 and 300–830 nm in external quantum efficiency (EQE) curves. We also analyzed the trends of photovoltaic properties of PSC, measured with solar simulator, validated with characterization of perovskite films. Furthermore, a beam-splitting PV-T system with PSC of different spectral response ranges was simulated and optimized using the experimental data. Results show that the beam-splitting PV-T system can achieve a theoretical solar-to-electric efficiency of 25.74%. This efficiency is higher than those of single CSP and single PSC, which are 23.97% and 17.27% respectively. The efficiency tends to decline with ratio of Br− increasing, because though the open-circuit voltage boosts and EQE remains unchanged, the declined quality of PSC reduces the fill factor, which is detrimental to the overall efficiency. Even though introducing Br− cannot increase the overall efficiency, the changed spectral response range brings about shift of the optimum cutoff wavelength, and the efficiency just slightly decreases with low ratio of Br− mixing, which is important for matching beam splitters in beam-splitting PV-T system.

The UK Government’s attention has shifted towards decarbonising heat with particular research attention towards transitioning away from natural gas as a heating fuel, moving to decarbonised alternatives, such as hydrogen. This research will serve useful to looking at the best way to decarbonise heating in, but not specifically, rural energy and heating, where this work focuses on. Over 20% of UK homes are off the natural gas grid of which 76% use heating oil, coal and other similar fuels to heat their homes. Little research has been conducted to look at what technologies and approaches can work best for rural decarbonisation. There are unique challenges to rural energy, and particularly heating, that have been scarcely addressed in government policy and wider research, such as older infrastructure, older non-standard buildings and a generally older demographic that add extra consideration to community understanding and cost to decarbonise rural energy. This paper will look at the specific challenges of rural energy decarbonisation and potential approaches to mitigate some of these challenges, such as consolidating electricity, heating and transport fuels with fewer types of biofuels and e-fuels (e.g. green hydrogen) to optimise the unit cost of energy across the different modes. Furthermore, investigation into what techno-socio-economic factors may further encourage greener energy technology adoption, not just on a carbon basis, but on what may work better for people in the community to reach net zero, will be highlighted. This will be different to those on key factors, such as what heating fuel is currently used, the fabric of the building (how heat efficient/insulated the building currently is), the cost margins of current technologies, community attitude and affordability towards green technologies. A point often said is that there is no one size fits all approach. This paper will look at how to approach rural energy heat decarbonisation. Finding heat pump and wood biomass fuels/technologies to be both the least polluting and most cost effective when solely considering operational cost and emissions across three building types.

Hydrogen becomes one of key elements to achieve global carbon neutrality goal. Among various sources of hydrogen, the commercial potential of natural hydrogen found in the subsurface has received many attentions from the energy industry. Understanding wave propagation and AVO effects of hydrogen-bearing reservoir within complex structural settings is critical to evaluate potential hydrogen prospects with seismic data. This study aims to investigate the wave field characteristics of potential hydrogen-bearing geological structures with a forward modeling approach.

This article proposes a technique for determining the optimal capacities of solar photovoltaic (PV) and battery energy storage (BES) systems for grid-connected commercial buildings in Malaysia. The method utilizes real-time data on load patterns, solar irradiance, ambient temperature, and Malaysian power rates to establish the lowest life cycle cost (LCC) of the PV and BES systems over a 20-year lifespan. The proposed system configuration includes rule-based energy management with peak shaving. The study also considers limitations on the maximum export power of Malaysian commercial buildings for optimization. The proposed system uses the price of electricity as an index, and a case study demonstrates that it reduced the cost of electricity by 34.25% for the commercial building case with the C1 tariff. Additionally, annual energy consumption and peak demand are reduced by 20.53% and 15.25%, respectively, while selling 10,128.6274 kWh of electricity back to the grid. Further, the optimal sizing capacities of PV and BES for Malaysian commercial buildings are presented and evaluated which provides a general demonstration for customers. This article is relevant to the field of electrical engineering and offers practical solutions for optimizing solar PV and BES systems in grid-connected commercial buildings, reducing the cost of electricity, and minimizing energy consumption.

Effervescent atomization is a type of twin-fluid atomization in which a small amount of gas is bubbled into the liquid before it is ejected from the atomizer. The technique of directly bubbling gas into the liquid stream inside the atomizer body differs significantly from other methods of twin-fluid atomization (either internal or external mixing) and results in significant performance improvements in terms of smaller drop sizes and even relatively lower injection pressures. The internal two-phase flow behaviour inside the injector will significantly affect the downstream spray characteristics. The current study involves the numerical investigation of two-phase flow behaviour inside an effervescent atomizer using a 2-D axi-symmetric computational domain. Both mixture and VOF multiphase approaches with URANS (standard, RNG, and realizable k-ε) turbulence models have been adopted in this study. The effect of turbulence models on the two-phase flow evolution in the injector has been studied with a GLR (gas-to-liquid ratio) of 0.08%. The liquid is taken as ethanol, and atomizing gas is nitrogen for the current study.

The development of promising offshore wind farms contributes to the cleanliness of the power system, and hydrogen (H2) is a potential energy carrier to address the challenges of delivering power from long-distance offshore wind farms. In this work, a life cycle evaluation (LCA) model for green hydrogen production from offshore wind farms is developed. The greenhouse gas emissions (GHG) and fossil energy consumption of compressed hydrogen (G-H2), liquefied hydrogen (L-H2) and liquid ammonia (L-NH3) as energy carriers are compared, and the sensitivity of GHG to wind farm load factor and transport distance is analysed. The results show that the G-H2 route has the lowest GHG emissions of 633.2 (gCO2eq/kg H2) and fossil energy consumption of 7.25 MJ/kg. In contrast, the L-NH3 route possesses the highest GHG emissions of 732.6 (gCO2eq/kg H2) and fossil energy consumption of 8.134 MJ/kg H2. In addition, the sensitivity coefficients of GHG emissions to transportation distance and annual load factor of offshore wind farms are investigated, and the sensitivity of the annual load factor reached between 0.9 and 1.4, following the order of L-H2 > L-NH3 > G-H2. In addition, the sensitivity coefficient of transportation distance is only reflected in G-H2 and reaches a remarkable value of 0.4 at 500 km. The results indicate that the G-H2 route has the lowest greenhouse gas emissions and fossil energy consumption at distances of less than 70 km offshore and that beyond 70 km, L-H2 is the preferred alternative. This work provides a comprehensive life cycle perspective to facilitate the selection of energy carriers for offshore hydrogen production.

Drag-dominated turbines play a key role in the application of urban windfarm and multi-flow direction tidal arrays because of their low cut-in speed and omnidirectional characteristics. A performance analysis study of Pinwheel and Savonius tidal turbines has been carried out using Computational Fluid Dynamics (CFD) software to define the optimal power coefficient (Cp) and Tip-Speed-Ratios (TSR). The classic Disk Actuator model assumes a fixed virtual disc with or without porous holes perpendicular to the inflow direction. This is unsuitable for drag-dominant turbine because of the rotating virtual disc of the rotor plate of a vertical-axis turbine, the unaccounted bypass flow interaction on the downstream flow boundary for a horizontal-axis turbine, and parasitic force acting on the rotor/support walls for both. Therefore, a more applicable model is required for the tidal turbine realm. The focus of this study is to propose a novel method to find the optimal TSR of a drag-dominant turbine with a cost-effective and user-friendly Moment Balancing algorithm. The CFD models were inspired and scaled from experimental findings in the literature review. Both models were made comparable using a parametric study to equalize the blockage area at 12%. After careful analysis of different solver settings, steady k-epsilon model was selected, and grid independence tests were conducted. V-shaped TSR matrix was developed with varying turbine rotational speeds and fluid inlet velocity, unlike previous works simulated at a fixed velocity. For Pinwheel and Savonius, the TSR range for simulations is 0.64–5.0 and 0.3–1.0 respectively. Thrust Moment (Acting) is calculated when the turbine is stationary, but the fluid motion exerts load and rotates it. Idle Moment (Resisting) is calculated when the turbine is rotating at a given speed and the water is stationary hence, a load is exerted on the turbine. Linear regression analysis was performed and coefficients for thrust and idle moment were calculated, thus, formulating an equation for the net moment of Pinwheel and Savonius. It is found that the power coefficient is maximum or zero when idle and thrust moment offset each other at the neutral point. The optimal TSR are found for Pinwheel at 2.37 and Savonius at 0.63 with 15.6% and 11.1% error rate respectively for experimental validation. Based on the findings, thrust and idle moment have a positive and negative quadratic relationship respectively with the inlet velocity. A hill-shaped curve is observed between power coefficient and TSR. The optimal TSR for Pinwheel is higher than Savonius, thereby a higher rotational and lower inlet speed should be adjusted accordingly and vice versa. The proposed algorithm is expected to improve and simplify an engineer’s understanding of the turbine’s optimal TSR by adjusting the rotor speed to suit the inlet flow case. The computational cost is greatly reduced through replacing net moment simulations by combining thrust and idle moment simulations. Upon commercial launch of the algorithm, the tidal energy development will become robust and more affordable.

Despite the rise of renewables and EVs, IC engines are expected to be useful in applications requiring a reliable, compact, remote and scalable power source. As a result, improving engine performance and addressing environmental concerns associated with NOx and soot emissions becomes important. The current study explores viability of alternative fuels like H2 and CNG to achieve lower emissions and better efficiency. CFD simulations using CONVERGE software with detailed chemical kinetics are used to model engine combustion and predict heat release rate and pressure rise for a canonical geometry in spark ignited mode. In the first part of the study, gasoline fuel combustion is simulated to validate the CFD tool with previous experiments and predictions and good agreement has been observed. In the second part of the project, simulations for the same engine geometry were performed with the fuels as: pure CH4, 50/50 CH4/H2 by volume and pure H2. Due to high-flame speed and extremely low ‘minimum ignition energy requirement’ of H2, combustion duration is low and pressure rise is extremely steep. Consequently, for H2 case the piston ends up working against the hot gases, while for a similar operating condition, peak pressure is considerably low for pure methane. The engine with the HCNG blend as the fuel can achieve higher cycle work than either of the two cases by the fuels compensating for shortcomings of the individual fuels: H2 and CH4. The amount of NOx produced was predicted to undergo a non-linear increment with hydrogen enrichment. Therefore, higher engine power can be achieved in the 50/50 mixture with less than half of the NOx produced for pure hydrogen.

Methanol-blended gasoline has gained momentum as an alternative fuel for direct injection spark ignition engines. In modern gasoline direct injection (GDI) injectors, the fuels are generally injected into combustion chamber at high injection temperature and pressure, but accurate estimation of the blended fuel properties at these conditions remains a challenge. To overcome the challenge, molecular dynamics simulation, which has the potential to find the properties at engine-relevant conditions can be used. In this study, the molecular dynamics (MD) simulation is performed to predict the transport properties of methanol-octane fuel blends (M15: 15% Methanol & 85% Octane and M85: 85% Methanol & 15% Octane) such as density, viscosity, and diffusion coefficient at different temperatures (303–363 K) and pressures (1–200 bar). The MD simulation results for neat methanol, n-octane, and methanol + octane binary mixture was validated with the NIST and experimental data available in the literature and found a good agreement with density (absolute error: <1%) and viscosity (absolute error: <5%). The MD simulation results for both M15 and M85 blend show that density and viscosity decrease with temperature and increase with pressure, while diffusivity increases with temperature and decreases with pressure. These molecular fuel properties at wide range of pressure and temperature could be useful in analyzing fuel spray, ignition, and combustion characteristics of spark ignition (SI) engines.

This research work attempts to explore the combined effect of engine load and compression ratio (CR) on the performance, combustion, and emission characteristics of a 3.5 kW diesel engine utilizing simulated biogas (SBG), simulated producer gas (SPG), and SPG-SBG mixture under dual fuel (DF) mode. The compositions of the gaseous fuels are prepared based on the volumetric percentage of the individual gas components and are inducted into the engine cylinder using a novel venturi-type air-gas mixer. For the preparation of SBG, methane (CH4) and carbon dioxide (CO2) are mixed at a 70:30 ratio. Similarly, hydrogen (H2) and carbon monoxide (CO) are also mixed at a 70:30 ratio for the preparation of SPG. Again, H2 and CO at a 50:50 ratio are mixed with a 70:30 ratio of CH4 and CO2 to simulate the SPG-SBG mixture. Engine experiments are executed at five different loads, viz. 20%, 40%, 60%, 80%, and 100%, and four different CRs, viz. 16, 17, 17.5, and 18, at a standard injection timing (IT) of 23° BTDC. Maximum brake thermal efficiency (BTE) and pilot fuel replacement (PFR) are obtained at 100% load and CR of 18. At this operating condition, SBG, SPG, and SPG-SBG mixture showed a BTE of 20.66%, 20.94%, and 22.11% respectively, and PFR of 89.76%, 81.44%, and 84.36% respectively. The combustion data indicated a decrement in ignition delay (ID) with an increase in CR. It has also been observed that SBG, SPG, and SPG-SBG mixture showed an average decrease of 10.51%, 9.93%, and 11.16%, respectively, of unburned carbon monoxide (CO) emission when the CR is raised from 16 to 18. Similarly, an average unburned hydrocarbon (HC) emission reduction is obtained to be 16.13%, 13.01%, and 20.07% for SBG, SPG, and SPG-SBG mixture, respectively.

Proton exchange membrane fuel cell (PEMFC) has drawn the world’s attention for its advanced features of zero-emission, high-power density and low noise. However, water management significantly influences the performance of proton exchange membrane fuel cells (PEMFC). Gas diffusion layer (GDL) is an important pathway for water transport in the PEMFC. The presence of excessive liquid water in the cathode GDL will cause “water flooding” and block the reactant transport to catalyst sites. Therefore, it is critical to improve water management in the GDL to enhance cell performance. Specially, perforated GDL is an effective way to improve the water transport process. In this study, the volume of fluid (VOF) method is used to numerically simulate the transport process of liquid water in perforated GDL with different hydrophilicity distributions. The GDL geometry is reconstructed based on the stochastic parameter method. With our numerical method, the GDLs with cylindrical and conical perforations are compared to understand the water transport process. Additionally, the effects of perforation shapes and fiber hydrophilicity gradient distributions on the water transfer process also been discussed. The results show that the GDL with conical perforation (bottom surface close to the inlet), liquid water tends to diffuse along the though-plane direction. The GDL with cylinder perforation are most effective to transporting fluid water. The water saturation in GDLs with gradient wettability is higher than that in GDLs without gradient wettability.

The present study investigated the effect of various substrate mixing ratios of cattle dung (CD) and vegetable waste (VW) on biogas yield, both with and without biochar addition. Two sets of batch-type anaerobic biochemical methane potential experiments were conducted at different percentages of CD:VW mixing ratio viz. 70:30, 50:50, and 30:70% on a mass basis simultaneously in triplicate, maintaining substrates/water ratio 1:1 using the 1000 ml glass reagent bottles with a working volume of 700 ml for the retention time of 45 days. The set-I experiment comprised a mixer of CD and VW only, whereas set-II comprised CD and VW with the addition of 15 g/l biochar. The biochar was prepared from the water hyacinth using a pyrolyser at a pyrolysis temperature of 550 °C. The set-II digesters with CD:VW mixing ratios of 70:30, 50:50, and 30:70% were obtained to be 28.52, 17.91, and 18.29% higher volume of methane yield than the digesters with respective mixing ratios for set-I. The higher methane generation rate was observed for set-II as the biochar can provide an adequate surface area for colonisation of the microbial flora, which was detected in the analysis of its morphology and pore surface. Improvement in pH value was also observed with the addition of biochar due to the alkaline nature of biochar. Comparing cumulative methane yield, the digester with CD and VW mixing ratio of 70:30% was detected to be the best for all sets of experiments. The maximum specific cumulative methane yields for these digesters without and with biochar addition were 132.57 mlCH4/gVS and 183.07 mlCH4/gVS, respectively. A kinetic study was also carried out for all sets of experiments using a modified Gompertz model. The correlation coefficient (R2) value was above 0.9, indicating that the modified Gompertz model and experimental data agreed reasonably well. The present study introduces a sustainable waste management method that can be applied to rural areas for clean energy generation.

A data analytics model for a cooling management system is proposed to find the optimal adjustment of target temperatures and air-conditioner fan mode to maximize energy efficiency while maintaining residents’ comfort. The ambient scenarios and usage of air conditioner data can be collected from sensors and Internet of Things (IoT) devices installed in an occupied home. Long-short-term memory (LSTM) algorithms have been developed to predict the power consumption of the air conditioner and the indoor temperature and humidity from ambient scenario data and adjustment data of target temperatures and air-conditioner fan mode. A particle swarm optimization (PSO) algorithm has been developed to be capable of selecting the target temperatures and the air-conditioner fan mode that are most appropriate for energy savings while controlling comfort for the occupants by using a predicted mean vote (PMV) as a criterion. The implementation results indicate that the proposed data analytics model can effectively predict the power consumption of the air conditioner and the indoor ambient conditions and succeed in finding the best adjustment case for the air conditioner in any different ambient scenarios, thereby increasing the potential for home energy savings.

Waste accumulation on land and water bodies is a global concern impeding the sustenance of human and marine lives. The waste-related data indicates that the share of non-recycled waste that are ending up in the landfill sites every year is substantial. Several incineration and gasification units exist in India. However, plasma based gasification centers are still not common in India and globally this technology has proven to be a potential solution to handle the menace of waste accumulation. The high-temperature (1000–5000 K) environment of thermal plasma is assumed to be breaking down complex waste molecules into much simpler forms which is substantiated by emission levels in producer gas much lower than the regulated environmental norms. Theoretical and experimental studies have been carried out addressing multiple aspects of the plasma-based waste gasification, such as composition of product gases post-gasification, dominant plasma characteristics and plasma-waste interaction. Such a study can provide the foundation of an analysis tool which can help planning and establishment of several plasma-based waste treatment plants across the country.

Latent heat of vaporization (LHvap) is a crucial property in internal combustion engines (ICEs). It affects the cylinder temperature (T), ignition delay, NOx emission and other phenomenon in ICEs. With the increase in global warming, use of alternative fuels in ICEs such as gasoline–ethanol blends, gasoline–methanol blends have become evident. Another important use of LHvap values is encountered while performing the spray combustion analysis of blended fuels using Computational Fluid Dynamics (CFD). This paper investigates the use of various Machine Learning (ML) techniques to predict the latent heat of vaporization (LHvap) for blended fuels. The algorithms used were Linear Regression (LR), Polynomial Regression (PR), Support Vector Machine (SVM), K Nearest Neighbors (KNN), Decision Tree (DT), and Random Forest (RF). The features used were Temperature, Blend Ratio, Molecular weight, Carbon (wt%), Hydrogen (wt%), and Oxygen (wt%). For training the algorithms, data was collected from several published research papers with LHvap values of gasoline-alcohol fuel blends and various diesel blends. The model was initially trained with data pertaining to gasoline-alcohol blends only, which showed that LR performed better than other algorithms in predicting both LHvap, with a coefficient of determination (R2 score) of 90% and Mean Absolute Percentage Error (MAPE) of 6.2%. This behavior was attributed to the linearity in the data, as most of the data points were of gasoline-alcohol blends with different blend ratios and temperatures. However, when more datapoints were included such as various oxygenated blends of diesel, it was found that RF performed much better than other algorithms, with an R2 score of 95.7% and MAPE of 6.8%. Furthermore, additional features were considered such as vol% of paraffins, aromatics, olefins, iso-paraffins, cyclo-paraffins, napthenes along with original features. The results showed that predictions improved with adding these features as the R2 score improved for all algorithms. It could be summarized that with availability of more data, the performance of RF algorithm could further improve. Thermodynamic relations have been attempted to correlate predictions of latent heat of vaporization with vapor pressure data of the blends.

The ever-increasing demand for renewables in the energy system has drawn attention to technologies capable of minimizing the effect of renewables’ intermittency and shave-off the generation-demand imbalance of the system. Energy storage can help to level peaks in energy demand, thus reducing wastage due to excess capacity during off-peak demand periods. Among the storage media, thermal energy storages (TES) have a large variety of applications, ranging from solar energy utilization and power peaking to industrial waste heat storage. In this study, data collected from an operating commercial stratified tank are used to validate a 2-D axisymmetric CFD model. Temperature profiles at various heights are collected throughout one month with a one-minute refresh rate. The model replicating the tank is generated in COMSOL Multiphysics® and validated by emulating the registered charging phases of the real storage, thus comparing the temperature layers before and after the charging occurs. The model is then employed to optimize the stratification efficiency of the tank, by varying the logics applied to pinpoint optimal values of both inlet water temperature and velocity. The study aims to minimize the MIX number, parameter often utilized in literature to identify the ability of the storage to generate and preserve optimal temperature stratification. Said dimensionless number is evaluated by accounting for the momentum of energy of the different temperature layers found in the water tank. Therefore, a discretization of the thermal storage in five sub-volumes, each of them characterized by the presence of an installed thermocouple, was defined. Finally, the experimental MIX number has been evaluated for the aforementioned temperature profiles.

We present a novel design for a High Vacuum Photovoltaic-Thermal (HV PV-T) device, which combines photovoltaics and thermal energy conversion in a flat-plane architecture. Our design aims to reduce convective heat loss via high-vacuum encapsulation, whilst maintaining high electrical efficiency even at elevated temperatures. This system is well suited for converting solar energy into thermal energy and effectively meeting thermal demands in industrial processes, especially those needing temperatures up to 150 °C, like boiling and pasteurization. The PV-T system consists of three primary components: a glass covering and a metallic vessel, which keep the device under high vacuum conditions (p < 0.1 Pa), and the central PV-T device. The PV-T device comprises four essential layers namely, a Transparent Conductive Oxide (TCO), a Perovskite-based PV cell, a Solar Absorber (SA), and a copper substrate. These layers are welded onto a copper piping to allow heat extraction via heat transfer fluid. For a comprehensive evaluation of the proposed PV-T device performances, we developed a one-dimensional numerical model in MATLAB. The observed performance outcomes are affected by radiative losses, which depend on both the operating temperature $$\left( {T_{op} } \right)$$ T op and the emittance of the TCO layer $$\left( {\varepsilon_{TCO} } \right)$$ ε TCO . Therefore, we conducted a performance analysis by changing these two parameters within the appropriate ranges of (25 $$\div$$ ÷ 175) °C and (0.05 $$\div$$ ÷ 0.45). The annual thermal and electrical outputs of our PV-T system were evaluated, employing hourly meteorological data from Amsterdam (Netherlands), Naples (Italy), and Doha (Qatar). In addition, a comparative analysis was conducted with commercial High-Vacuum Flat Plate Solar-Thermal (HVFP ST) collectors and PV panels. The results indicate that at a temperature of 100 °C and with emittance values below 0.21, the annual thermal yields surpass 503 kWh/(m2 year) for Amsterdam, 941 kWh/(m2 year) for Naples, and 1278 kWh/(m2 year) for Doha. Furthermore, annual electrical generation stands at 158 kWh/(m2 year) for Amsterdam, 234 kWh/(m2 year) for Naples, and 288 kWh/(m2 year) for Doha. In terms of economic viability, our study shows promising outcomes. In Naples’ climate, for an annual thermal demand of 26 GWh, a cost margin of 248 €/m2 is granted to our suggested HV PV-T system to achieve the same Simple Pay-Back time as the HVFP ST solution. In such a situation, the HV PV-T option can lower annual CO2 emissions by 58% more than the HVFP ST solution.

Industrial facilities are dynamic businesses that consume more than one energy in their production processes. This study developed an energy efficiency roadmap, which should be followed by energy management in an industrial enterprise with textile production, and the business process was evaluated. The energy efficiency assessment of the enterprise is a key indicator for corporate management strategies and is a process that must be managed. Indeed, energy efficiency analyses were primarily evaluated with the GAP analysis, handled according to a framework, and the energy efficiency study was evaluated. In this context, a consumption analysis has been made for the last three years and a road map has been created for the target productivity potential based on consumption. According to the efficiency histogram developed accordingly, a target of 29.08% was determined for energy efficiency. The sustainable efficiency flow of the enterprise was evaluated along with a four-year action period, and the actions that needed to be developed were evaluated.

Airports, an important center of action for the aviation sector, can be seen as a key structural model for combating global climate change. The framework of energy management should be considered entropy management and sustainability, especially for these structural systems, which are multi-source for fossil resource consumption and have a mobile campus with all its elements. This study discussed the environmental indicators taken as a basis for an exemplary airport and the difficulties in front of the green transformation in the institutional structure. The study evaluated the effects of the indicators developed for control tools and the classical approaches. In this respect, it is seen that exergy destruction in airports has potential effects on green transformation as an environmental problem. The exergy approach can be used as a model to evaluate energy efficiency potential. In this context, it will be possible to define the potential for changing tools in energy consumption and to shape a target for decision processes. A target evaluation to be defined with the green transition for squares with a savings potential of up to 70% will make a significant contribution to road maps. At the end of the study, some suggestions are also presented for this purpose.

Optimum Load forecasting is an integral part of planning and operation of electric utility. Any positive or negative error at planning stage leads to utility losses. It is hard to reflect impact of all natural, man-made and global effects in models to give the true picture of power demand in future. Electricity demand forecasting techniques may consist of simple trend line extrapolations, statistical regression-based techniques, artificial intelligence based neural networks or fuzzy logic-based techniques and genetic algorithms, vector support machine and long experience based smart expert systems. All the forecasting methods assume continued supply of various types of fuels and generation development over time to meet the projected targets and goals. Sudden wars, attacks on power and energy lifelines, change in global policies, climate changes and out of blue-sky events render even the best predictions useless. If all possible events are considered but they do not happen then the resulting situations affect economic power system operation. Demand forecasting becomes even more difficult in polygeneration deficient utilities where multiple energy resources are used for generation of multiple energy vectors simultaneously. This paper attempts to estimate demand and line losses in energy deficient polygeneration utilities carrying out demand side management. Polygeneration is a sustainable energy concept that simultaneously produces multiple forms of energy, such as electricity, heat, and chemicals, from a single integrated system utilizing various input energies and conversion technologies. Random transition from seven to three or two stages tariffs without equating areas under the curves causes unregistered losses to power and gas utilities.

Light, heat and electricity are common types of energy demands at domestic level. Mankind uses food, fuel and electric energies to survive on the planet earth. Sun is the ultimate source of energy, which drives water, wood, wind and wave energy cycles and generates light and heat energy. Solar energy lives in vegetation in the form of hydrocarbon chemical bonds. Fossil fuels are also a form of old times stored solar energies. Anaerobic combustion of plants in subsurface under high temperature and pressure converts buried wood into coal, gas and oil. Burning of coal, gas and oil in power plants and vehicles causes air pollution and climate change due to massive 40 Gt CO2/y GHG emissions. Global warming is accelerating meltdown of midpole glaciers supplying fresh water to entire Asia. Geopolitics has decelerated efforts slowing down climate change by walking out of Paris Accord. Humankind, since last 200,000 (Nature) to 315,000 (Morocco) years, has been facing global cooling but first time faced the global warming. Global warming is acutely upsetting global food supply chain, transport and ecosystems worldwide. We are already late in starting GHG emission reductions and developing adaptive strategies to cope with global warming, which should no more be delayed to take action. Combustion of coal and GM crops stubble causes air pollution in South Asia. Rice crops stubbles burning in Punjab increases Air Quality Index (AQI) more than 1000 points every year in October and November. Transboundary air pollution laws similar to water and carbon particles as smog melt glaciers, cause heat waves, choke filters in thermal power plants and deteriorates dielectric strength of high voltage insulators. Scientists and environmentalists propose gradual transition from fossil fuels to renewable energy systems. This paper analyzes air pollution causes and overviews long journey from mire to fire and wave to wire.

The Proton Exchange Membrane Fuel Cell (PEMFC) is a highly efficient green electrochemical conversion device using hydrogen energy. The gas diffusion layer (GDL) is the thickest part of the membrane electrode in PEMFC. The microstructure of GDL takes an important role in the migrations of gas, liquid, heat, and electron. Optimizing the GDL microstructure is crucial to improve the gas–liquid two-phase flowing behavior and enhance the PEMFC performance. Therefore, a kind of GDL with the ordered pore structure is designed with the template method to provide large pores channel for water elimination and normal small pores channel for gas delivery. Then, a comparison between our ordered GDL and commercial GDL has been performed using a PEMFC test platform under different working conditions, including relative humidity and temperature. The results show that the ordered GDL can improve the PEMFC performance under high humidity compared with commercial GDL, which is mainly because the large pore channel in ordered GDL can enhance water transfer. The peak power density of the fuel cell with ordered GDL is increased by 10.8% compared with commercial GDL at the same high relative humidity. However, with a decrease in relative humidity, the ohmic loss gradually replaces the mass transfer optimization caused by the perforation, and the performance of the fuel cell with ordered GDL shows a downward trend. Compared with laser perforation, the current work proposed the template method to prepare the ordered GDL, which is low-cost and convenient and provides an alternative concept for batch preparation of ordered GDL.