Search Results (240)

In recent years, recovering precious and base metals such as silver and copper from end-of-life products has become a fundamental factor in the sustainable development of many countries. This not only supports environmental goals but is also a profitable economic activity. Therefore, in this study, we investigate the recovery of silver and copper from an end-of-life photovoltaic panel powder using an alternative leaching system containing sulfuric acid and ferric sulfate instead of nitric acid-based leaching systems, which are susceptible to producing hazardous gases such as NO_x. To obtain this goal, a series of experiments were designed with the Central Composite Design (CCD) approach using Response Surface Methodology (RSM) to evaluate the effect of reagent concentrations on the leaching rate. The leaching results showed that high recovery rates of silver (>85%) and copper (>96%) were achieved at room temperature using a solution containing only 0.2 M sulfuric acid and 0.15 M ferric sulfate. Analysis of variance was applied to the leaching data for silver and copper recovery, resulting in two statistical models that predict the leaching efficiency based on reagent concentrations. Results indicate that the models are statistically significant due to their high R² (0.9988 and 0.9911 for Ag and Cu, respectively) and the low p-value of 0.0043 and 0.0003 for Ag and Cu, respectively. The models were optimized to maximize the dissolution of silver and copper using Design Expert software. Full article

Italy strives to meet its renewable energy targets for 2030 and 2050, with photovoltaic (PV) technology playing a central role. However, the push for increased solar adoption, spurred by past incentive schemes such as “Conto Energia” and “Superbonus 110%”, raises long-term challenges related to PV waste management. In this study, we present a multi-scale approach to forecast End-of-Life (EoL) PV waste across Italy’s 20 regions, aiming to support national circular economy strategies. Historical installation data (2008–2024) were collected and combined with socio-economic and energy-related indicators to train a Backpropagation Neural Network (BPNN) for regional PV capacity forecasting up to 2050. Each model was optimised and validated using R² and RMSE metrics. The projections indicate that current trends fall short of meeting Italy’s decarbonisation targets. Subsequently, by applying a Weibull reliability function under two distinct scenarios (Early-loss and Regular-loss), we estimated the annual and regional distribution of PV panels reaching their EoL. This analysis provides spatially explicit insights into future PV waste flows, essential for planning regional recycling infrastructures and ensuring sustainable energy transitions. Full article

This study combines convolutional neural network (CNN) recognition technology, Greenwich engineering software, and statistical yearbook methods to evaluate rural solar, wind, and biomass energy resources in pilot cities in China, respectively. The CNN method enables the rapid identification of the available roof area, and Greenwich software provides wind resource simulation with local terrain adaptability. The results show that the capacity of photovoltaic power generation reaches approximately 15.63 GW, the potential of wind power is 458.3 MW, and the equivalent of agricultural waste is 433,900 tons of standard coal. The city is rich in wind, solar, and biomass resources. By optimizing the hybrid power generation system through genetic algorithms, wind energy, solar energy, biomass energy, and coal power are combined to balance the annual electricity demand in rural areas. The energy trends under different demand growth rates were predicted through the LEAP model, revealing that in the clean coal scenario of carbon capture (WSBC-CCS), clean coal power and renewable energy will dominate by 2030. Carbon dioxide emissions will peak in 2024 and return to the 2020 level between 2028 and 2029. Under the scenario of pure renewable energy (H_WSB), SO₂/NO_x will be reduced by 23–25%, and carbon dioxide emissions will approach zero. This study evaluates the renewable energy potential, power system capacity optimization, and carbon emission characteristics of pilot cities at a macro scale. Future work should further analyze the impact mechanisms of data sensitivity on these assessment results. Full article

The transition to renewable energy requires sustainable solar manufacturing through optimized Production–Usage–Recycling (PUR) cycles, where electromagnetic (EM) sensing offers non-destructive monitoring solutions. This review categorizes EM methods into low- (<100 MHz) and medium-frequency (100 MHz–10 GHz) techniques for material evaluation, defect detection, and performance optimization throughout the solar lifecycle. During production, eddy current testing and impedance spectroscopy improve quality control while reducing waste. In operational phases, RFID-based monitoring enables continuous performance tracking and early fault detection of photovoltaic panels. For recycling, electrodynamic separation efficiently recovers materials, supporting circular economies. The analysis demonstrates the unique advantages of EM techniques in non-contact evaluation, real-time monitoring, and material-specific characterization, addressing critical sustainability challenges in photovoltaic systems. By examining capabilities and limitations, we highlight EM monitoring’s transformative potential for sustainable manufacturing, from production quality assurance to end-of-life material recovery. The frequency-based framework provides manufacturers with physics-guided solutions that enhance efficiency while minimizing environmental impact. This comprehensive assessment establishes EM technologies as vital tools for advancing solar energy systems, offering practical monitoring approaches that align with global sustainability goals. The review identifies current challenges and future opportunities in implementing these techniques, emphasizing their role in facilitating the renewable energy transition through improved resource efficiency and lifecycle management. Full article

The integration of renewable energy sources, battery energy storage, and electric vehicles into industrial systems unlocks new opportunities for reducing emissions and improving sustainability. However, the coordination and management of these new technologies also pose new challenges due to complex interactions. This paper proposes a methodology for designing a holistic energy management system, based on advanced digital twins and optimization techniques, to minimize the cost of supplying industry loads and electric vehicles using local renewable energy sources, second-life battery energy storage systems, and grid power. The digital twins represent and forecast the principal energy assets, providing variables necessary for optimizers, such as photovoltaic generation, the state of charge and state of health of electric vehicles and stationary batteries, and industry power demand. Furthermore, a two-layer optimization framework based on mixed-integer linear programming is proposed. The optimization aims to minimize the cost of purchased energy from the grid, local second-life battery operation, and electric vehicle fleet charging. The paper details the mathematical fundamentals behind digital twins and optimizers. Finally, a real-world case study is used to demonstrate the operation of the proposed approach within the context of the waste collection and management industry. The study confirms the effectiveness of digital twins for forecasting and performance analysis in complex energy systems. Furthermore, the optimization strategies reduce the operational costs by 1.3%, compared to the actual industry procedure, resulting in daily savings of EUR 24.2 through the efficient scheduling of electric vehicle fleet charging. Full article

Conjugated polymers (CPs) offer many attractive features for photodiodes and photovoltaics, including solution processability, ease of scale-up, light weight, low cost, and mechanical flexibility. CPs have a wide range of energy gaps; thus, the choice of the specific polymer determines the optimum operational wavelength range. However, there are relatively few CPs with a strong absorption in the blue region of the spectrum where the human eye is most sensitive (440 to 470 nm) and none with an energy gap at 2.75 eV (450 nm), which corresponds to the peak of the CIE-1931 z(λ) color-matching function and the dominant blue light emission wavelength in computer and smartphone displays. Blue-light detectors in this wavelength range are important for light hazard control, sky polarization studies, and for blue-light information devices, where 450 nm corresponds to the principal emission of GaN-based light sources. We report on a new CP called Atums Green (AG), which shows promising characteristics as a blue-light photodetection polymer optimized for exactly this range of wavelengths centered around 450 nm. We built and measured a simple photodetector made from spin-coated films of AG and showed that its photosensitivity can be improved by the addition of asphaltene, a low-cost carbonaceous waste product. Full article

This study investigates the hydrothermal synthesis of calcium silicate hydrate (C-S-H) from photovoltaic (PV) waste glass and carbide sludge as a strategy for resource recovery and sustainable chromium removal from wastewater. Waste-derived precursors were co-ground, blended at controlled Ca/Si molar ratios (0.8, 1.0, 1.2), and hydrothermally treated at 180 °C for 96 h to yield C-S-H with tunable morphology and crystallinity. Comprehensive characterization using XRD, FT-IR, SEM-EDX, and UV-Vis spectroscopy revealed that a Ca/Si ratio of 1.0 produced a well-ordered tobermorite/xonotlite structure with a high surface area and fibrous network, which is optimal for adsorption. Batch adsorption experiments showed that this material achieved rapid and efficient Cr(III) removal, exceeding 90% uptake within 9 h through a combination of surface complexation, ion exchange (Ca²⁺/Na⁺ ↔ Cr³⁺), and precipitation of CaCrO₄ phases. Morphological and structural evolution during adsorption was confirmed by SEM, FT-IR, and XRD, while EDX mapping established the progressive incorporation of Cr into the C-S-H matrix. These findings highlight the viability of upcycling industrial waste into advanced C-S-H sorbents for heavy metal remediation. Further work is recommended to address sorbent regeneration, long-term stability, and application to other contaminants, providing a foundation for circular approaches in advanced wastewater treatment. Full article

Next-generation recycling technologies must be urgently innovated to tackle huge volumes of spent batteries, photovoltaic panels or printed circuit boards (WPCBs). Current e-waste recycling industrial technology is dominated by traditional recycling technologies. Herein, ionic liquids (ILs), deep eutectic solvents (DESs) and promising oxidizing additives that can overcome some traditional recycling methods of metal ions from e-waste, used in our works from last year, are presented. The unique chemical environments of ILs and DESs, with the application of low-temperature extraction procedures, are important environmental aspects known as “Green Methods”. A closed-loop system for recycling zinc and manganese from the “black mass” (BM) of waste, Zn-MnO₂ batteries, is presented. The leaching process achieves a high efficiency and distribution ratio using the composition of two solvents (Cyanex 272 + diethyl phosphite (DPh)) for Zn(II) extraction. High extraction efficiency with 100% zinc and manganese recovery is also achieved using DESs (cholinum chloride/lactic acid, 1:2, DES 1, and cholinum chloride/malonic acid, 1:1, DES 2). New, greener recycling approaches to metal extraction from the BM of spent Li-ion batteries are presented with ILs ([N_8,8,8,1][Cl], (Aliquat 336), [P_6,6,6,14][Cl], [P_6,6,6,14][SCN] and [Benzet][TCM]) eight DESs, Cyanex 272 and D2EHPA. A high extraction efficiency of Li(I) (41–92 wt%) and Ni(II) (37–52 wt%) using (Cyanex 272 + DPh) is obtained. The recovery of Ni(II) and Cd(II) from the BM of spent Ni-Cd batteries is also demonstrated. The extraction efficiency of DES 1 and DES 2, contrary to ILs ([P_6,6,6,14][Cl] and [P_6,6,6,14][SCN]), is at the level of 30 wt% for Ni(II) and 100 wt% for Cd(II). In this mini-review, the option to use ILs, DESs and Cyanex 272 for the recovery of valuable metals from end-of-life WPCBs is presented. Next-generation recycling technologies, in contrast to the extraction of metals from acidic leachate preceded by thermal pre-treatment or from solid material only after thermal pre-treatment, have been developed with ILs and DESs using the ABS method, as well as Cyanex 272 (only after the thermal pre-treatment of WPCBs), with a process efficiency of 60–100 wt%. In this process, four new ILs are used: didecyldimethylammonium propionate, [N_10,10,1,1][C₂H₅COO], didecylmethylammonium hydrogen sulphate, [N_10,10,1,H][HSO₄], didecyldimethylammonium dihydrogen phosphate, [N_10,10,1,1][H₂PO₄], and tetrabutylphosphonium dihydrogen phosphate, [P_4,4,4,4][H₂PO₄]. The extraction of Cu(II), Ag(I) and other metals such as Al(III), Fe(II) and Zn(II) from solid WPCBs is demonstrated. Various additives are used during the extraction processes. The Analyst 800 atomic absorption spectrometer (FAAS) is used for the determination of metal content in the solid BM. The ICP-OES method is used for metal analysis. The obtained results describe the possible application of ILs and DESs as environmental media for upcycling spent electronic wastes. Full article

Steam is widely used in industry as a heat carrier for thermal processes and is primarily generated by gas-fired steam boilers. The decarbonization of industrial thermal demand relies on the capability of clean and renewable technologies to provide steam through reliable and cost-effective systems. Concentrating solar thermal technologies are attracting attention as a heat source for industrial steam generation. In addition, electricity-driven high-temperature heat pumps can provide heat using either renewable or grid electricity by upgrading ambient or waste heat to the required temperature level. In this study, linear Fresnel solar collectors and high-temperature heat pumps driven by photovoltaics are considered heat sources for steam generation in industrial processes. Energetic and economic analyses are performed across the European countries to assess and compare their performances. The results demonstrate that for a given available area for the solar field, solar thermal systems provide a higher annual energy yield in southern countries and at lower costs than heat pumps. On the other hand, heat pumps driven by photovoltaics provide higher annual energy for decreasing solar radiation conditions (central and northern Europe), although it leads to higher costs than solar thermal systems. A hybrid scheme combining the two technologies is the favorable option in central Europe, allowing a trade-off between the costs and the energy yield per unit area. Full article

This study investigates a hybrid renewable energy system combining the municipal solid waste (MSW) gasification and solar photovoltaic (PV) for electricity generation in Lobito, Angola. A fixed-bed downdraft gasifier was selected for MSW gasification, where the thermal decomposition of waste under controlled air flow produces syngas rich in CO and H₂. The syngas is treated to remove contaminants before powering a combined cycle. The PV system was designed for optimal energy generation, considering local solar radiation and shading effects. Simulation tools, including Aspen Plus v11.0, PVsyst v8, and HOMER Pro software 3.16.2, were used for modeling and optimization. The hybrid system generates 62 GWh/year of electricity, with the gasifier contributing 42 GWh/year, and the PV system contributing 20 GWh/year. This total energy output, sufficient to power 1186 households, demonstrates an integration mechanism that mitigates the intermittency of solar energy through continuous MSW gasification. However, the system lacks surplus electricity for green hydrogen production, given the region’s energy deficit. Economically, the system achieves a Levelized Cost of Energy of 0.1792 USD/kWh and a payback period of 16 years. This extended payback period is mainly due to the hydrogen production system, which has a low production rate and is not economically viable. When excluding H₂ production, the payback period is reduced to 11 years, making the hybrid system more attractive. Environmental benefits include a reduction in CO₂ emissions of 42,000 t/year from MSW gasification and 395 t/year from PV production, while also addressing waste management challenges. This study highlights the mechanisms behind hybrid system operation, emphasizing its role in reducing energy poverty, improving public health, and promoting sustainable development in Angola. Full article

The aim of this study was the hydrothermal leaching of silver from waste monocrystalline silicon (m-Si) and polycrystalline silicon (p-Si) photovoltaic panel (PV) cells using organic acids, namely oxalic acid (OA) and citric acid (CA). Before leaching, two different pretreatment procedures were applied. First, the fluoropolymer backsheet was manually removed from the panel pieces and, then, the samples were subjected to high-temperature heating for the thermal degradation of the ethylene vinyl acetate (EVA) polymer. When removal by hand was not feasible, the second pretreatment procedure was followed by toluene immersion to remove the EVA and backsheet and separate the cells, glass, and films. After pretreatment, 4 M HCl leaching was applied to remove the aluminum layer from the cells. The remaining cells were subjected to hydrothermal leaching with organic acids to extract the silver. Several hydrothermal parameters were investigated, such as acid concentration (1-1.5-2 M), processing time (60-105-150 min), and temperature (150-180-210 °C), while the liquid-to-solid (L/S) ratio was fixed at 30 mL: 1 g, based on preliminary tests. Response surface methodology (RSM) was applied to optimize the hydrothermal leaching parameters. The optimized parameters were 210 °C, 95 min, 2 M CA or 210 °C, 60 min, 1 M OA. OA was more effective in Ag leaching than CA. The results were compared to HNO₃ leaching. The green leaching of silver from end-of-life PV panels with organic acids is an environmentally beneficial route. Full article

As photovoltaic (PV) installations expand globally, effective recycling of end-of-life crystalline silicon solar cells has become increasingly important, including the recovery of valuable metals such as silver (Ag) and aluminium (Al). Traditional nitric acid-based chemical leaching methods, although effective, present environmental challenges due to the generation of hazardous nitrogen oxide (NO_x) emissions. To address these concerns, this study investigated alternative hydrometallurgical leaching strategies. Two selective treatments (NaOH for Al, and NH₃ + H₂O₂ for Ag) and one simultaneous treatment (HNO₃ + H₂O₂) were evaluated for metal recovery efficiency. All methods demonstrated high recovery efficiencies, achieving at least 99% for both metals within 60 min. The investigated methods effectively suppressed NO_x emissions without compromising leaching efficiency. These findings confirm that hydrometallurgical leaching techniques incorporating hydrogen peroxide can achieve efficient and environmentally safer recovery of silver and aluminium from solar cells, providing valuable insights into the development of more sustainable recycling practices for photovoltaic waste management. Full article

With the rapid development of smart grid technologies, communication networks have become the core infrastructure supporting control and energy optimization in microgrids. However, the excessive reliance of microgrid control on communication networks faces dual challenges: On one hand, the high-frequency information exchange under traditional periodic communication patterns causes severe waste of network resources; on the other hand, cyberattacks may cause information loss, abnormal delays, or data tampering, which can ultimately lead to system instability. To address these challenges, this paper investigates the secure dynamic integral event-triggered stabilization of photovoltaic microgrids under multimodal deception attacks. To address the communication resource constraints in photovoltaic (PV) microgrid systems, a dynamic integral-event-triggered scheme (DIETS) is proposed. This scheme employs average processing of historical state data to filter out redundant triggering events caused by noise or disturbances. Simultaneously, a time-varying triggering threshold function is designed by integrating real-time system states and historical information trends, enabling adaptive adjustment of dynamic triggering thresholds. In terms of cybersecurity, a secure control strategy against multi-modal deception attacks is incorporated to enhance system resilience. Subsequently, through the Lyapunov–Krasovskii functional and Bessel–Legendre inequality, collaborative design conditions for the controller gain and triggering matrix are formed as symmetric linear matrix inequalities to ensure system stability. The simulation results demonstrate that DIETS recorded only 99 triggering events, achieving a 55.2% reduction compared to the normal event-triggered scheme (ETS) and a 52.6% decrease relative to dynamic ETS, verifying the outstanding communication effectiveness of DIETS. Full article

Waste-to-energy (WTE) is considered the most promising method for municipal solid waste treatment. An integrated energy system (IES) with carbon capture systems (CCS) and power-to-gas (P2G) can reduce carbon emissions. The incorporation of a “green-carbon” offset mechanism further enhances renewable energy consumption. Therefore, this study constructs a WTE-IES hybrid system, which conducts multi-dimensional integration of IES-WTP, CCS-P2G, photovoltaic (PV), wind turbine (WT), multiple energy storage technologies, and the “green-carbon” offset mechanism. It breaks through the limitations of traditional single-technology optimization and achieves the coordinated improvement of energy, environmental, and economic triple benefits. First, waste incineration power generation is coupled into the IES. A mathematical model is then established for the waste incineration and CCS-P2G IES. The CO₂ produced by waste incineration is absorbed and reused. Finally, the “green-carbon” offset mechanism is introduced to convert tradable green certificates (TGCs) into carbon emission rights. This approach ensures energy demand satisfaction while minimizing carbon emissions. Economic incentives are also provided for the carbon capture and conversion processes. A case study of an industrial park is conducted for validation. The industrial park has achieved a reduction in carbon emissions of approximately 72.1% and a reduction in the total cost of approximately 33.5%. The results demonstrate that the proposed method significantly reduces carbon emissions. The energy utilization efficiency and system economic performance are also improved. This study provides theoretical and technical support for the low-carbon development of future IES. Full article

Decarbonizing production activities is a critical task in the transition towards carbon neutrality. Traditional carbon footprint accounting tools, such as life-cycle assessment (LCA) and the Greenhouse Gas Protocol, primarily quantify direct and indirect emissions but offer limited guidance on actionable reduction strategies. To address this gap, this study proposes a comprehensive life-cycle carbon footprint optimization framework that integrates LCA with a mixed-integer linear programming (MILP) model. The framework, while applicable to various production contexts, is validated using a university campus as a case study. In 2023, the evaluated university’s net carbon emissions totaled approximately 24,175.07 t CO₂-eq. Based on gross emissions (28,306.43 t CO₂-eq) before offsetting, electricity accounted for 66.09%, buildings for 15.55%, fossil fuels for 8.67%, and waste treatment for 8.46%. Seasonal analysis revealed that June and December exhibited the highest energy consumption, with emissions exceeding the monthly average by 19.4% and 48.6%, respectively, due to energy-intensive air conditioning demand. Teaching activities emerged as a primary contributor, with baseline emissions estimated at 5485.24 t CO₂-eq. Optimization strategies targeting course scheduling yielded substantial reductions: photovoltaic-based scheduling reduced electricity emissions by 7.00%, seasonal load shifting achieved a 26.92% reduction, and combining both strategies resulted in the highest reduction, at 45.95%. These results demonstrate that aligning academic schedules with photovoltaic generation and seasonal energy demand can significantly enhance emission reduction outcomes. The proposed framework provides a scalable and transferable approach for integrating time-based and capacity-based carbon optimization strategies across broader operational systems beyond the education sector. Full article