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Buildings account for a substantial share of global energy use, yet the adoption of advanced optimal control strategies remains limited due to high computational costs and the difficulty of safe deployment. This paper presents a fully Python-based, data-driven deep reinforcement learning (DRL) supervisory control framework that leverages gray box surrogate modeling and Imitation Learning to overcome these barriers. The novelty of this work lies in the integration of an ontology-based Twin4Build surrogate model with Imitation Learning and Deep Reinforcement Learning, enabling efficient training of building control policies in a low-cost environment before transfer to a high-fidelity BOPTEST emulator. Results demonstrate that the trade-off of using a lower-accuracy surrogate accelerates training by a factor of 11 compared to high-fidelity models. Furthermore, the RL agent successfully learned load-shifting and peak-shaving strategies, eliminating start-up power spikes and achieving energy savings of up to 28.9%. Beyond substantial energy reductions, this pipeline yields a calibrated digital twin suitable for ongoing building services like anomaly detection, presenting a scalable path for real-world smart building optimization. Full article

Modern agriculture has to alleviate extremes in water demand and/or water waste. In this regard, this work showcases how soil moisture instruments can be combined with low-end microcontrollers, energy-efficient communication protocols, single-board computers, flow and pressure sensors, and purpose-built actuators to form a synergistic platform able to generate and study realistic irrigation scenarios. These scenarios, potentially emulating anomalies such as clogged emitters or pipe leaks with a satisfactory time granularity of a few minutes, provide valuable data that pave the way for the creation of intelligent models intercepting water misuse events and/or irrigation failures. The proposed system utilizes widely available, well-documented, low-cost components to form a functioning whole which is optimized for outdoor, low-power, low-maintenance and long-term operation and is accessible remotely via typical end-user devices. Two drip irrigation points were set up, each having a TEROS 12 and a TEROS 10 instrument placed at different depths, while a prototype water flow/pressure control and report system was developed. All modules sent data in real time, via LoRa, to a central node implemented using a Raspberry Pi for further processing and to make them widely available via common network infrastructures, also provisioning for remote scenario invocation. The system does not claim to achieve specific irrigation water savings, but it contributes to maintaining/increasing the benefits of modern irrigation practices (such as drip irrigation). This goal is served by emulating a wide variety of irrigation events and by gathering and studying the corresponding data. These multimodal data are collected at a frequency of a few minutes, reflecting key irrigation-specific parameters with an accuracy better than or equal to 3%. The exact steps for specific hardware and software component interoperation are clearly explained, allowing other teams of researchers and/or university educators worldwide to be inspired and benefit from platform replication. Full article

Lithium is a critical mineral for traction batteries and a cornerstone of the sustainable transition toward low-carbon transportation. Understanding the supply–demand dynamics and resource-saving potential of lithium is essential for advancing circular economy goals and ensuring the long-term stability of the electric vehicle (EV) industry. This study develops an integrated lithium forecast framework by coupling a System Dynamics (SD) model with dynamic Material Flow Analysis (MFA) and multi-scenario pathways. To ensure robust conclusions, the model is validated against historical data, and a multi-level sensitivity analysis is conducted to address the inherent uncertainties of evolving socio-technical assumptions over a ten-year horizon. The simulation results reveal that under the baseline scenario, China’s EV stocks and annual lithium demand will grow by 8.3 and 4.7 times from 2024 to 2035, respectively. This rapid expansion poses a significant sustainability challenge, as cumulative demand will deplete 50–71% of China’s domestic lithium reserves by 2035. Despite a projected supply–demand gap of 110–120 kt/yr, the study identifies critical pathways for resource decoupling and circularity. Technology-driven interventions, such as enhancing energy density and extending battery lifespan, can reduce primary lithium demand by up to 18.9%. Furthermore, optimizing the closed-loop recycling system can contract the supply–demand gap by 31–39%, demonstrating the pivotal role of secondary resource recovery in building a resilient supply chain. Despite this reduction, a persistent reliance on international markets remains inevitable. These findings provide a quantified scientific foundation for policymakers, emphasizing that lithium security requires a synergistic transition from volume-based subsidies to resource efficiency mandates and standardized, formal closed-loop recycling systems. Full article

The anammox technology, as an efficient and energy-saving denitrification method, has been widely used in the field of wastewater treatment. Nevertheless, this process faces two key challenges in actual operation, namely the fluctuation of nitrite substrate supply and the residual nitrate, which greatly limits its promotion and application in a wider range. Although the traditional combined process of partial denitrification/anammox (PD/A) can generate nitrite substances, the coexistence of heterotrophic microorganisms and organic carbon sources in the system may have a significant inhibitory effect on the proliferation of Anammox bacteria. The sulfur-oxidizing bacteria (SOB) involved in the sulfur autotrophic denitrification process (SAD) have overlapping ecological niches with Anammox microorganisms and have stable nitrite enrichment characteristics. In view of this, sulfur-oxidizing bacteria are regarded as a potential candidate for combining with the Anammox process. However, the denitrification efficiency of this process is often restricted by the low solubility and poor bioavailability of substrates. At the same time, there are significant research gaps and data deficiencies regarding the key operating parameters for autotrophic short-range denitrification using elemental sulfur to achieve nitrite accumulation and the coupling application of this process with other wastewater treatment technologies. In view of this, this study is committed to comprehensively sorting out and evaluating the existing optimization methods of the elemental sulfur autotrophic denitrification process, while providing an in-depth analysis of its mechanism of action and environmental control factors. At the same time, this study also carried out innovative exploration on the modification process of the sulfur element from the frontier perspective of materials science and pointed out the key directions for subsequent optimization of the construction path of the elemental sulfur autotrophic denitrification system and for improving the denitrification process efficiency. In summary, this study systematically discusses the mechanism of action, practical application, and improvement scheme of PS⁰AD. Full article

In event-based neuromorphic processing, computer vision finds an efficient alternative capable of optimizing computational and energy resources, inspired by the dynamics of biological neural systems. In the development of real-time processing systems, it is crucial to visually represent the information captured by sensors and to explore its content with precision. Thus, machine learning models are implemented with the capability of being deployed on hardware devices with limited capabilities, depending on the intended purpose, ensuring savings in computational resources. The aim of this work was to evaluate the limits of the implemented neuron model, leaky-integrate and fire (LIF), for fitting convolutional layers of a neural network. To this end, the characteristics of the LIF neuron model used are summarized, as well as the details of its implementation in a hardware design, using configurable parameters. The experimental phase considered two convolution approaches to compare performance, Matlab R2022a software and a spiking convolutional processor for an FPGA, using sample recordings from the MNIST-DVS dataset and Sobel kernels for edge detection. The results reflect that the number of spikes generated by both approaches is very similar and their distribution by frame addresses is directly proportional. Full article

Against the backdrop of escalating global climate challenges, minimizing carbon emissions while enhancing energy efficiency in rice production has emerged as a core pathway toward achieving agricultural carbon neutrality. A two-year field study conducted in the Yangtze River Delta evaluated three rice cultivation practices: the farmers’ practice pattern (FPP), surface-applied controlled-release fertilizer with machine transplanting (S-CRF), and side-deep applied controlled-release fertilizer with machine transplanting (SD-CRF). Compared to FPP and S-CRF, SD-CRF increased grain yields by 11.3% and 9.2%, respectively, while reducing total energy input by 2.5% and 2.4%. It lowered the carbon intensity of production by 9.7% and 8.2% relative to FPP and S-CRF, primarily through reducing fertilizer/labor-associated carbon inputs and enhancing carbon-use efficiency via higher yield. Economically, SD-CRF outperformed traditional practices, achieving an 81.8% increase in net income and a 37.4% higher benefit-to-cost ratio compared with FPP, respectively, driven by labor cost savings and improved productivity. Notably, SD-CRF reduces labor input by 40.0% compared with FPP, simplifies fertilization operations, lowers farmers’ operational technical thresholds, and effectively boosts their economic income. Data envelopment analysis (DEA) further validated SD-CRF’s superior eco-efficiency, highlighting its dual advantage in balancing yield enhancement and environmental sustainability. Further clarification of SD-CRF application technical indicators, refinement of agronomic practices and machinery efficiency, and promotion of the integrated system’s synergistic benefits and scalable adoption are required to support global sustainable food systems and carbon neutrality goals. Full article

This study investigated energy-efficient renovation strategies for traditional Thai wooden houses through constructing a demo building and computational assessments. The study addresses the challenges posed by climate change and increasing comfort demands, which have led to increasing use of air conditioning in these historically significant structures. A demo building, designed to replicate a traditional Thai house, was constructed, featuring two rooms: one insulated with magnesium-bonded Typha boards and the other uninsulated. The effectiveness of the insulation was evaluated through hygrothermal simulations and real-time temperature and humidity measurements. The frequently occurring problem of missing measurement data was solved by approximately determining unknown variables through iterative adjustment and comparison of simulation results with measured data. The results indicate that the Typha-insulated room maintained a stable indoor climate, with significantly lower energy consumption from air conditioning than the uninsulated room. Since the air conditioning system was insufficiently powerful in the uninsulated room, it is not possible to quantify the energy savings precisely using measurement technology. However, subsequent hygrothermal simulations enabled a comparative assessment of the energy-saving potential of various measures. Depending on insulation measures and manner of room use, savings of 75–80% could be achieved. Such computational and practical studies can contribute to the preservation of historic buildings. Full article

Wheat is rich in carbohydrates and proteins but is susceptible to pest infestation and microbial contamination during storage. Owing to itself high efficiency, energy savings, and lack of chemical residues, electron beam irradiation (EBI) has been widely applied for disinfesting and sterilizing cereals and has been shown to influence dough quality. Notably, starch is present within complex wheat flour systems during processing, and its irradiation response may differ from that of purified systems. In this study, the effects of different EBI doses (0, 3, 6, 9 and 12 kGy) on the multiscale structure and physicochemical properties of wheat starch isolated from irradiated dough were systematically investigated, and key analytical techniques such as Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and rheological analysis were employed to elucidate the mechanisms underlying its impact on the dough thermomechanical behavior of dough. The results demonstrated that EBI weakened gluten–starch interactions and disrupted gluten network the continuity and compactness of the gluten network, resulting in significant dough farinography and pasting property changes. Compared with those of the control group, the dough development and stability time of the 12 kGy sample decreased from 3.920 and 6.465 to 0.970 and 1.290, respectively (p < 0.05). Moreover, irradiation induced cracks on the starch surface, reduced its molecular weight, and disrupted its crystallinity and short-range order. These changes resulted in decreases in the thermal stability level and swelling capacity of starch, while increasing its solubility. A correlation analysis revealed that the starch chain length distribution, molecular weight, molecular order, and pasting properties are key determinants of EBI-induced dough quality changes. This study provides theoretical insights into the applicability of EBI in the context of wheat flour storage and quality modulation. Full article

Processes for generating clean hydrogen from waste plastics through thermochemical methods such as pyrolysis and gasification are a promising solution for both waste management and clean energy initiatives. Then, this derived hydrogen powers the fuel cell, which produces electricity that can be directly fed to charge electric vehicles (EVs). Although this complex process has many challenges related to energy efficiency during the conversion processes—starting from the generation of hydrogen from thermochemical processes and hydrogen storage and followed by fueling the fuel cells and charging EV infrastructure—the simplistic conceptual modeling developed for this research demonstrates how an ecosystem of such processes can be made feasible commercially. Clean hydrogen generated using known techniques reported in the literature is promising for commercialization, but harnessing hydrogen from plastics offers additional benefits, such as reducing greenhouse gas (GHG) emissions. Overall, the feasibility of clean hydrogen using this methodology is not limited by potential cost inefficiencies, especially when savings from GHG emissions reduction are taken into account. EVs have become commercially viable thanks to high-energy-density Li-ion batteries. And therefore, research continues to optimize charging performance through the integration of renewable energy and battery storage systems. This study examines another potential of clean hydrogen: its use as a power source in grids, especially V-2-G (vehicle-to-grid) systems. Additionally, direct current (DC) power from a fuel cell powers an EV charger at DC input voltages for e-ambulances. In particular, this designed system operates on DC voltages throughout the power system, combining high-voltage direct current (HVDC) lines, renewable energy sources, DC-DC converters, DC EV chargers, and other supporting components. The literature review identified gaps in plastics production, waste management, and processes for converting them into useful energy. The presented model is a stepping stone towards a novel, innovative process for clean hydrogen production to power electric vehicle charging infrastructure for emergency response systems in healthcare, thereby improving public safety. The limitations of the study would be governed by the effective establishment of locations where waste management services are performed (for example, landfills) and adoption by local government authorities with deregulated power systems. Full article

In the current work, a novel heat pump drying system with precise control of temperature and humidity of drying medium was developed and the impacts of drying temperature and humidity on the drying characteristics of apple slices and energy consumption of drying system were investigated. Experimental results indicated that the temperature and relative humidity (RH) of drying medium have a significant impact on drying efficiency and operating performance. During the first hour of the drying process, the heat pump drying of apple slices exhibited the highest drying rate throughout the entire process at a temperature of 40~50 °C and a relative humidity of 30~60%. And then the apple slices drying was in a falling-rate drying stage. When the relative humidity of the drying medium exceeded 50%, the final moisture content of the material increased significantly and exceeded 20% (dry basis, d.b.). Increased air medium temperature and humidity enhance the dehumidification rate of the evaporator. When the drying temperature was maintained at 40–60 °C, the condensation rate at 60% RH was 3.5–10 times that at 30% RH. The increased dehumidification rate significantly promoted the energy efficiency. The specific moisture extraction rate (SMER) was 2.53 kg/(kW·h) at 60 °C and 60% RH, which is 3.4 times that at 30% RH. It was appropriate to adopt high-temperature and high-humidity conditions in the early drying stage to improve drying energy efficiency. Meanwhile, the relative humidity should be reduced to promote moisture removal from the material in the late drying stage. The obtained results provided theoretical methods for the energy-saving control of heat pump drying for fruits. Full article

The construction industry faces persistent challenges in productivity, efficiency, and sustainability. Digital twin (DT) technology has emerged as a promising pathway for lifecycle optimisation, yet its construction adoption remains limited. Key barriers include fragmentation across project phases, weak data continuity at handover, and conceptual ambiguity between DT and Building Information Modelling (BIM). This systematic literature review analyses 160 peer-reviewed studies (2018–2026) selected from 463 Scopus records using a PRISMA-guided process and inter-rater reliability testing (Cohen’s κ = 0.83). The review clarifies that DTs extend beyond BIM in three ways: they enable bidirectional, automated physical-digital data exchange; integrate heterogeneous real-time sources such as IoT sensors and operational systems; and maintain lifecycle continuity from design through to end-of-life. Select advanced implementations report notable performance gains. These include rework and logistics reductions of up to 80%, cost savings of approximately 5%, schedule acceleration of around two months, energy reductions of 15–30%, and maintenance cost reductions of 10–25%. These figures reflect case-level outcomes from high-performing pilots and should not be read as typical industry benchmarks. Broader adoption remains constrained by interoperability gaps, data quality challenges, digital maturity deficits, misaligned stakeholder incentives, and paper-based regulatory environments. DTs represent a socio-technical transformation, not a standalone technology upgrade. Realising their potential requires coordinated progress in standards development, governance frameworks, collaborative delivery models, and workforce capability. Future research should focus on scalable interoperability, longitudinal lifecycle value validation, human-centred adoption strategies, and sustainability assessment methods to support evidence-based diffusion of DTs in the built environment. Full article

The increasing variability of residential demand, combined with the expansion of distributed generation and electric vehicles, has introduced new challenges to the stability of Smart Grids (SGs). Centralized management models lack the flexibility required to operate under these conditions, reinforcing the need for scalable and data-driven architectures. This study proposes an energy management solution based on microservices, supported by hybrid communication in Low Power Wide Area Networks (LPWAN), integrating Long Range Wide Area Network (LoRaWAN) and LoRaMESH to enhance connectivity, local resilience, and reliability in data acquisition for Internet of Things (IoT) and Demand Response (DR) applications. A prototype composed of a Smart Meter (SM), a Data Aggregation Point (DAP), and a Concentrator (CON) was evaluated in a controlled environment, achieving Packet Delivery Rates above 97%, an average RSSI of −92 dBm, and a Signal-to-Noise Ratio close to 9 dB, validating the robustness of the hybrid communication. At a larger scale, data from 5567 households in the Low Carbon London (LCL) project were used to generate representative Load Profiles (LPs) through seven aggregation and clustering techniques, consistently identifying the 18:00–21:00 interval as the critical peak, with demand reaching up to 42% above the daily average. Fourteen load shifting algorithms were evaluated, and the Hybrid Adaptive Algorithm based on Intention and Resilience (HAAIR), proposed in this work, achieved the best overall performance with a 1.83% peak reduction, US$65.40 in cost savings, a reduction of 60 kg of CO₂, a Comfort Loss Index of 0.04, resilience of 9.5, and reliability of 0.98. The results demonstrate that the integration of hybrid LPWAN communication, modular microservice-based architecture, and adaptive DR strategies driven by Artificial Intelligence (AI) represents a promising pathway toward scalable, resilient, and energy-efficient SGs. Full article

Isobutane is a key feedstock for alkylate production. For separating an equimolar isobutane/n-butane mixture with 2 mol% ethane, two conventional designs are reported in the literature: a single water-cooled condenser (SC) and a dual condenser system with refrigeration (DC). This study proposes two vapor recompression retrofit configurations, SC-VR and SC-PHVR (with preheating), to improve energy efficiency and enable electrification. Economic and environmental performance were evaluated using total annualized cost (TAC) and CO₂ emissions. Compared with SC and DC schemes, SC-VR reduces CO₂ emissions by 49 and 52%, while SC-PHVR delivers higher reductions of 64 and 66%. A sensitivity analysis of electricity prices across 3-, 5-, and 10-year payback periods indicates the most favorable performance at 10 years. At 16.67 USD/GJ, SC-PHVR lowers TAC by 22 and 25%; in contrast, SC-VR provides marginal savings. At 24.03 USD/GJ, SC-VR is not economically competitive, whereas SC-PHVR continues to outperform the conventional cases, with TAC reductions of 8% and 4%. Both retrofit options significantly reduce emissions, with SC-PHVR offering the best economic performance. Finally, the proposed configurations enable the complete electrification of the de-isobutanizer system, eliminating reliance on fossil-based thermal utilities, which allows the use of renewable sources in line with the decarbonization efforts. Full article

With the rapid adoption of dishwashers in China’s households, the consequent growth in energy and water consumption presents new challenges for energy conservation, emission reduction, and water resource management in China. To address this, this study adopts the concept of the energy–water nexus to analyze the factors influencing dishwasher usage behaviors and quantifies the co-benefits of energy and water conservation through questionnaire surveys, establishing a household dishwasher energy and water accounting model. The findings reveal that dishwasher usage behaviors are influenced by dietary habits—greater oil usage in cooking leads to higher frequency of use and a greater tendency to select intensive wash modes, thereby increasing energy and water consumption. It is projected that by 2030, promoting energy-efficient dishwashers could achieve annual savings of approximately 390 million kWh of electricity and 4 million m³ of water. Furthermore, shifts in dietary habits offer significant potential for achieving co-benefits in energy and water conservation. Encouraging households to adopt low-oil cooking practices could yield additional annual savings of 5.14 billion kWh of electricity and 9.57 million m³ of water. Based on these results, this paper puts forward policy recommendations to facilitate the coordinated management of household energy and water conservation. Full article

Heating, Ventilation, and Air Conditioning (HVAC) systems used in modern buildings are among the largest contributors to energy consumption. Therefore, it is necessary to carefully balance between thermal comfort and energy efficiency when operating these systems. This study proposes a Symbolic Discrete Controller Synthesis (SDCS) approach for HVAC management that simultaneously enforces comfort-band constraints at the supervisory level and optimizes energy efficiency. Unlike traditional continuous controllers tuned per zone, the proposed method coordinates zone-level actuation through discrete power levels and node-level constraints (including an aggregate peak cap), exploiting thermal inertia to redistribute service over time without increasing comfort-band violations. Experimental evaluations on a multi-zone building model demonstrate that the SDCS approach provides comparable small comfort violations and provides superior energy savings when benchmarked against Model Predictive Control (MPC) and traditional Proportional-Integral-Derivative (PID) controllers. These results highlight the potential of SDCS as a robust and scalable solution for sustainable building management and energy-aware HVAC coordination in multi-zone buildings. Full article