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This paper explores the significant role of Wastewater Treatment Plants (WWTPs) in achieving environmental sustainability, with a particular focus on enhancing energy efficiency, resource recovery, and water reuse. WWTPs are crucial for removing pollutants and recovering resources from wastewater, thereby protecting public health and biodiversity. However, they are also associated with high operational costs, substantial carbon footprints, and energy-intensive processes. This article delves into various strategies and technologies to overcome these challenges, aiming to transform WWTPs from energy consumers to energy-efficient resource recovery hubs. Techniques such as anaerobic digestion and the use of advanced oxidation processes and microbial fuel cells are investigated for their potential in energy recovery and efficiency enhancement. Success stories from around the globe are highlighted to demonstrate the feasibility of transitioning to energy-positive WWTP operations. The integration of water reuse systems is also discussed, highlighting recent advancements that enable treated wastewater to be repurposed for agricultural, industrial, and potable uses, thereby promoting sustainability and water conservation. This paper emphasizes the importance of integrating cutting-edge energy management practices to minimize environmental impacts, reduce operational costs, and contribute to a more sustainable water sector. [ABSTRACT FROM AUTHOR]

The green transition is a challenge for the Polish economy and energy sector. In this paper, the expert method of analysis was applied and findings revealed that the challenges and prerequisites for an effective transformation of the power sector mainly include technical, technological and organizational issues of energy production and use. The provision of electricity at competitive prices and with a low carbon footprint, for individual consumers and industry, is a prerequisite for maintaining the well-being of the population and ensuring the competitiveness of domestically produced goods. The ambitious climate policy goals of the European Union require immediate action and call for radical changes in the Polish energy sector; in the coming years, it must drastically reduce the amount of energy produced from fossil fuels and replace it with so-called green energy from renewable sources. The main purpose of this article was to highlight the need to modify Poland's energy policy until 2040 in order to make it more consistent with the ambitious climate goals of the European Union. This article also shows that Poland's energy transition must include a shift from fossil fuels to renewables, while ensuring that energy security is stabilized by the current energy and generation resources. To this end, we discuss the issues of creating reserves in the national energy system for the entire period of Poland's energy transition. [ABSTRACT FROM AUTHOR]

Amidst the swift progression of microelectronics and Internet of Things technology, wearable devices are gradually gaining ground in the domains of human health monitoring. Recently, human bioenergy harvesting has emerged as a plausible alternative to batteries. This paper delves into harvesting human inertial energy that stimulates inertial masses through human motion and then transmutes the motion of the inertial masses into electrical energy. The inertial energy harvester is better suited for low‐frequency and irregular human motion. This review first identifies the sources of human motion excitation that are compatible with inertial energy harvesters and then provides a summary of the operating principles and the comparisons of the commonly used energy conversion mechanisms, including electromagnetic, piezoelectric, and triboelectric transducers. The review thoroughly summarizes the latest advancements in human inertial energy‐harvesting technology that are categorized and grouped based on their excitation sources and mechanical modulation methods. In addition, the review outlines the applications of inertial energy harvesters in powering wearable devices, medical health monitoring, and as mobile power sources. Finally, the challenges faced by inertial energy‐harvesting technologies are discussed, and the review provides a perspective on the potential developments in the field. [ABSTRACT FROM AUTHOR]

Modernizing traditional power grids into smart grids represents a significant advancement in improving efficiency and sustainability within the energy sector. However, integrating renewable energy sources and the intricate network of interconnected devices has presented new challenges, especially in grid stability. Anticipating and maintaining grid stability ensures a continuous energy supply and prevents potential disruptions. This research paper explores the smart grid stability issue through machine learning. Two distinct scenarios are examined. In the first scenario, the problem is approached as a binary classification task, where grid states are categorized as stable or unstable, a well-established method for addressing grid stability. In the second scenario, an effort is made to predict continuous values that reflect the degree of grid stability. These continuous values are subsequently mapped to a conditional function, facilitating stability detection based on a predefined threshold. To address data imbalance, two balancing strategies, specifically the Synthetic Minority Over-sampling Technique (SMOTE) and K-means SMOTE, are employed in each scenario. The analysis covers a range of regressors and classifiers, focusing on tree-based models, boosting algorithms, ensemble learners, and Multilayer Perceptrons (MLP). The investigation results demonstrate that combining the K-means SMOTE strategy with the CatBoost Classifier in the first scenario achieves the highest accuracy, reaching an impressive 99.6%, and an exceptional ability to detect grid instability, achieving 100% accuracy. Furthermore, the results of the second scenario are promising, with accuracy rates reaching 99.4%, thanks to the utilization of a stacking ensemble learner. Notably, this study introduces Explainable AI, a groundbreaking initiative in this field, to delve into the inner workings of the proposed models and enhance their transparency. This pioneering effort marks a significant step forward in understanding and optimizing the predictive capabilities of machine learning models applied to smart grid stability. [Display omitted] • Reframing the smart grid stability problem as a regression task. • Underscoring the significance of predicting the grid stability value. • Assessing the impact of data balancing strategies on the prediction results. • Evaluating several classifiers and regressors for smart grid stability. • Achieving 100% instability detection accuracy using K-means SMOTE and CatBoost. • Pioneering eXplainable AI (XAI) techniques to enhance model transparency. [ABSTRACT FROM AUTHOR]

A universal strategy that integrates PAA grafting, ion exchange, and in-situ chemical reactions to synthesize photothermal materials for enhanced solar-driven interfacial evaporation and gradient materials for electricity generation, respectively. [Display omitted] • A universal strategy is proposed for the preparation of SDIE and FFIPG materials. • Synthesized nanoparticles or nanoplates are evenly spread on SDIE fabric fibers. • A combination of one-way flow and suspension mode is used for fluid evaporation. • All SDIE fabrics achieve excellent evaporation rates at 0.5 cm suspension height. • The fluid flow can constantly generate a potential difference across the FFIPG fabrics. In our contemporary society, addressing the increasingly pressing global resource shortage, particularly the scarcity of vital water resources and energy, is critical for human survival. This paper presents a strategy that integrates PAA grafting, ion exchange, and in-situ chemical reactions to synthesize photothermal materials for enhanced solar-driven interfacial evaporation and gradient materials for electricity generation, respectively. The obtained photothermal material demonstrates exceptional light absorption and photothermal conversion performance. By employing a unique combination of unidirectional flow and suspension mode for water evaporation, remarkable evaporation rates are achieved, with all samples exceeding 2.03 kg m-2h−1. The hybrid mode is also applied to saltwater treatment, where it is found that the evaporation rate is not significantly affected, regardless of the salt concentration. No change in the evaporation rate is observed even after 12 h of continuous treatment with 10 % brine, indicating the robustness and stable performance of the evaporative materials. For the synthesized gradient materials, the flowing fluid can establish a potential difference at both ends of the gradient fabric. The magnitude of open circuit voltage and short circuit current can be controlled by adjusting the measurement distance, the deposited nanoparticles with different polarity and the amount of charge carried. We acquire a maximum output of 209.7 mV using the gradient Cu-S/PAA-cotton fabric, which can be connected in series to power small electronic devices like a digital watch. This research not only advances the design and practical application of high-performance solar evaporators but also illuminates a novel pathway for developing electricity-generating materials. [ABSTRACT FROM AUTHOR]

Integrating PV solar cells with concentrators into window systems can not only generate electricity for a building, but also has the potential to enhance the thermal resistance of the building's windows without a significant sacrifice of light transmittance for passive daylight. A novel system, known as the crossed compound parabolic concentrator photovoltaic window, has been recently studied for its electrical properties. However, its thermal and optical performance, particularly in terms of the overall heat transfer coefficient (U-value) and total optical transmittance when integrated into a building, remains unexamined. These factors are crucial for predicting the system's impact on a building's energy efficiency and indoor comfort. Therefore, this paper aims to investigate the U-value of this window system under various temperature scenarios and the optical transmittance of the window at different incident angles. The thermal conductance was assessed through numerical simulations using a computational fluid dynamics model, which was validated by experimental measurements conducted in a large climate chamber. The optical transmittance was investigated using a validated 3D ray-tracing model. The total optical transmittance and electricity generation were calculated for typical sunny days in winter and summer under London's climate conditions. Additionally, alternative designs were developed to explore the impact of pitch between adjacent optics on the thermal conductance and optical transmittance of the window. The results showed that the window with a configuration of D x = D y = 5 mm (where D x and D y represent the horizontal and vertical pitches, respectively, between two adjacent solar optics) achieved the lowest U-value (2.566 W/m2·K). This U-value is slightly lower than that of the original window design, which has a U-value of 2.575 W/m2·K. The original window configuration with D x = D y = 1.77 mm produces the highest power output. Specifically, it generates 499.25 Wh/m2 on a typical sunny day in winter and 162.73 Wh/m2 on a typical sunny day in summer. However, it exhibits the lowest transmittance (14.6 % on a typical sunny day in winter and 25.2 % on a typical sunny day in summer, respectively), indicating that it is more suitable for buildings with a higher window-to-wall ratio to ensure an adequate amount of natural light. For buildings with a lower window-to-wall ratio, the CCPC-PV window should be designed with a larger horizontal pitch, such as 15 mm and 30 mm, to meet indoor illuminance requirements while also providing enhanced thermal performance and additional power output. [ABSTRACT FROM AUTHOR]

• Develop a model of the TEG module (TG12-8-01 L) in MATLAB/Simulink to check its electrical characteristics prior to the design of the cookstove. • Implement fuzzy logic-based MPPT to accurately track optimum power from the TEG module. • Develop a modified and affordable prototype of a thermoelectric generator based improved biomass cookstove (TIBC). • Assess the effectiveness of the developed TIBC prototype based on the comparative analysis. Most of the people in the rural areas cook their food in traditional biomass cookstove which emits a lot of emission that leads to poor health of the women and children. This paper points out a technology that utilises a thermoelectric generator (TEG), which converts thermal energy into electrical energy directly. A TEG is a small plate made of semiconductor p-n junctions connected electrically in series and thermally in parallel. The difference in temperature produced on the hot side and cold side of the TEG is proportional to the voltage generated. The TEG is integrated into the wall of the inner combustion chamber of the biomass cookstove with the help of heat plate and the cold side of the TEG is connected to a flower-type heat sink to maintain the temperature difference. The power generated by the TEG is used to drive a 12 V DC fan which helps to keep the cold side of the TEG relatively cooler and direct the same air inside the inner combustion chamber of the cook stove through holes to increase the air-to-fuel ratio. As a result, it improves the cookstove's combustion, thereby reducing emissions and increasing thermal efficiency. For testing the efficiency of the TEGs available in the market, a hit and trial method is used by taking TEGs of different specifications and connecting them to a hot side and a cold side test bed and then finally the performance is realized. Therefore, for every different TEG, a hardware has to be made for final conclusion of the result. To eliminate the hit and trial method, the electrical characteristics of thermoelectric generator module has been analysed by modeling a TEG module in MATLAB/Simulink and implementing it with fuzzy logic control (FLC) based MPPT utilizing a DC-DC boost converter with a constant as well as variable load. This fuzzy logic-based scheme exhibits fast dynamic response and precisely tracks the maximum power point (MPP) from TEG under varying load conditions. Based on the robust performance of the obtained simulation results, the TEG module is integrated and tested in the laboratory using a thermoelectric generator based improved biomass cookstove (TIBC). The novelty lies on the concept of using MATLAB/Simulink platform by modeling the FLC based MPPT incorporating boost converter to test the performance of TEG module for its integration with TIBC and eliminating traditional hit and trial method. This TIBC is able to power the fan directly within just 2–3 min using the electricity generated by the TEG module. The maximum power generated from the TIBC is 6.25 W using a single TEG. The fan requires 1.4 W and rest of the power was used to charge auxiliary battery. [ABSTRACT FROM AUTHOR]