Search Results (752)

The transition to smart, zero-carbon cities relies on advanced, sustainable energy solutions, with artificial intelligence (AI) playing a crucial role in optimizing renewable energy management. This study evaluates state-of-the-art AI models for solar power forecasting, emphasizing accuracy, reliability, and environmental sustainability. Using operational data from Benban Solar Park in Egypt and Sakaka Solar Power Plant in Saudi Arabia, two of the world’s largest solar installations, the research highlights the effectiveness of hybrid AI techniques. The hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model outperformed other models, achieving a Mean Absolute Percentage Error (MAPE) of 2.04%, Root Mean Square Error (RMSE) of 184, Mean Absolute Error (MAE) of 252, and R² of 0.99 for Benban, and an MAPE of 2.00%, RMSE of 190, MAE of 255, and R² of 0.98 for Sakaka. This model excels at capturing complex spatiotemporal patterns in solar data while maintaining low computational CO₂ emissions, supporting sustainable AI practices. The findings demonstrate the potential of hybrid AI models to enhance the accuracy and sustainability of solar power forecasting, thereby contributing to efficient, resilient, and zero-carbon urban environments. This research provides valuable insights for policymakers and stakeholders aiming to advance smart energy infrastructure. Full article

Temperature is a key physiological indicator of plant health, influenced by factors including water status, disease and developmental stage. Monitoring changes in multiple factors is helpful for early diagnosis of plant growth. However, there are a variety of complex light interference phenomena in the greenhouse, so traditional detection methods cannot meet effective online monitoring of strawberry health status without manual intervention. Therefore, this paper proposes a leaf soft-sensing method based on a thermal infrared imaging sensor and adaptive image screening Internet of Things system, with additional sensors to realize indirect and rapid monitoring of the health status of a large range of strawberries. Firstly, a fuzzy comprehensive evaluation model is established by analyzing the environmental interference terms from the other sensors. Secondly, through the relationship between plant physiological metabolism and canopy temperature, a growth model is established to predict the growth period of strawberries based on canopy temperature. Finally, by deploying environmental sensors and solar height sensors, the image acquisition node is activated when the environmental interference is less than the specified value and the acquisition is completed. The results showed that the accuracy of this multiple sensors system was 86.9%, which is 30% higher than the traditional model and 4.28% higher than the latest advanced model. It makes it possible to quickly and accurately assess the health status of plants by a single factor without in-person manual intervention, and provides an important indication of the early, undetectable state of strawberry disease, based on remote operation. Full article

Agricultural nondestructive testing technology is pivotal in safeguarding food quality assurance, safety monitoring, and supply chain transparency. While conventional optical methods such as near-infrared spectroscopy and hyperspectral imaging demonstrate proficiency in surface composition analysis, their constrained penetration depth and environmental sensitivity limit effectiveness in dynamic agricultural inspections. This review highlights the transformative potential of microwave technologies, systematically examining their operational principles, current implementations, and developmental trajectories for agricultural quality control. Microwave technology leverages dielectric response mechanisms to overcome traditional limitations, such as low-frequency penetration for grain silo moisture testing and high-frequency multi-parameter analysis, enabling simultaneous detection of moisture gradients, density variations, and foreign contaminants. Established applications span moisture quantification in cereal grains, oilseed crops, and plant tissues, while emerging implementations address storage condition monitoring, mycotoxin detection, and adulteration screening. The high-frequency branch of the microwave–millimeter wave systems enhances analytical precision through molecular resonance effects and sub-millimeter spatial resolution, achieving trace-level contaminant identification. Current challenges focus on three areas: excessive absorption of low-frequency microwaves by high-moisture agricultural products, significant path loss of microwave high-frequency signals in complex environments, and the lack of a standardized dielectric database. In the future, it is essential to develop low-cost, highly sensitive, and portable systems based on solid-state microelectronics and metamaterials, and to utilize IoT and 6G communications to enable dynamic monitoring. This review not only consolidates the state-of-the-art but also identifies future innovation pathways, providing a roadmap for scalable deployment of next-generation agricultural NDT systems. Full article

Continuous and accurate biomass measurement is a critical enabler for control, decision making, and optimization in modern plant production systems. It supports the development of plant growth models for advanced control strategies like model predictive control, and enables responsive, data-driven, and plant state-dependent cultivation. Traditional biomass measurement methods, such as destructive sampling, are time-consuming and unsuitable for high-frequency monitoring. In contrast, image-based estimation using computer vision and deep learning requires frequent retraining and is sensitive to changes in lighting or plant morphology. This work introduces a low-cost, load-cell-based biomass monitoring system tailored for vertical farming applications. The system operates at the level of individual growing trays, offering a valuable middle ground between impractical plant-level sensing and overly coarse rack-level measurements. Tray-level data allow localized control actions, such as adjusting light spectrum and intensity per tray, thereby enhancing the utility of controllable LED systems. This granularity supports layer-specific optimization and anomaly detection, which are not feasible with rack-level feedback. The biomass sensor is easily scalable and can be retrofitted, addressing common challenges such as mechanical noise and thermal drift. It offers a practical and robust solution for biomass monitoring in dynamic, growing environments, enabling finer control and smarter decision making in both commercial and research-oriented vertical farming systems. The developed sensor was tested and validated against manual harvest data, demonstrating high agreement with actual plant biomass and confirming its suitability for integration into vertical farming systems. Full article

Floating solar technology has gained significant attention as part of the global expansion of renewable energy due to its potential for installation in underutilized water bodies. Several countries, including the Netherlands, have initiated efforts to extend this technology from inland freshwater applications to open offshore environments, particularly within offshore wind farm areas. This development is motivated by the synergistic benefits of increasing site energy density and leveraging the existing offshore grid infrastructure. The deployment of offshore floating photovoltaic (OFPV) systems involves assembling multiple modular units in a marine environment, introducing operational risks that may give rise to safety concerns. To mitigate these risks, weather windows must be considered prior to the task execution to ensure continuity between weather-sensitive activities, which can also lead to additional time delays and increased costs. Consequently, optimizing marine logistics becomes crucial to achieving the cost reductions necessary for making OFPV technology economically viable. This study employs a simulation-based approach to estimate the installation duration of a 5 MWp OFPV plant at a Dutch offshore wind farm site, started in different months and under three distinct risk management scenarios. Based on 20 years of hindcast wave data, the results reveal the impacts of campaign start months and risk management policies on installation duration. Across all the scenarios, the installation duration during the autumn and winter period is 160% longer than the one in the spring and summer period. The average installation durations, based on results from 12 campaign start months, are 70, 80, and 130 days for the three risk management policies analyzed. The result variation highlights the additional time required to mitigate operational risks arising from potential discontinuity between highly interdependent tasks (e.g., offshore platform assembly and mooring). Additionally, it is found that the weather-induced delays are mainly associated with the campaigns of pre-laying anchors and platform and mooring line installation compared with the other campaigns. In conclusion, this study presents a logistics modeling methodology for OFPV systems, demonstrated through a representative case study based on a state-of-the-art truss-type design. The primary contribution lies in providing a framework to quantify the performance of OFPV installation strategies at an early design stage. The findings of this case study further highlight that marine installation logistics are highly sensitive to local marine conditions and the chosen installation strategy, and should be integrated early in the OFPV design process to help reduce the levelized cost of electricity. Full article

Increased food production demands, labor shortages, and environmental concerns are driving the need for innovative agricultural technologies. However, effective adoption depends critically on aligning robot innovations with the needs of farmers. This paper examines the alignment between the needs of farmers and the robotic systems used in row crop farming. We review current commercial agricultural robots and research, and map these to the needs of farmers, as expressed in the literature, to identify the key issues holding back large-scale adoption. From initial pool of 184 research articles, 19 survey articles, and 82 commercial robotic solutions, we selected 38 peer-reviewed academic studies, 12 survey articles, and 18 commercially available robots for in-depth review and analysis for this study. We identify the key challenges faced by farmers and map them directly to the current and emerging capabilities of agricultural robots. We supplement the data gathered from the literature review of surveys and case studies with in-depth interviews with nine farmers to obtain deeper insights into the needs and day-to-day operations. Farmers reported mixed reactions to current technologies, acknowledging efficiency improvements but highlighting barriers such as capital costs, technical complexity, and inadequate support systems. There is a notable demand for technologies for improved plant health monitoring, soil condition assessment, and enhanced climate resilience. We then review state-of-the-art robotic solutions for row crop farming and map these technological capabilities to the farmers’ needs. Only technologies with field validation or operational deployment are included, to ensure practical relevance. These mappings generate insights that underscore the need for lightweight and modular robot technologies that can be adapted to diverse farming practices, as well as the need for farmers’ education and simpler interfaces to robotic operations and data analysis that are actionable for farmers. We conclude with recommendations for future research, emphasizing the importance of co-creation with the farming community to ensure the adoption and sustained use of agricultural robotic solutions. Full article

Redwood (Sequoia sempervirens (Lamb. ex D. Don) Endl.) is a fast-growing, long-lived conifer native to a narrow coastal zone along the western seaboard of the United States. Redwood can accumulate very high amounts of carbon in plantation settings and continuous cover forestry (CCF) represents a highly profitable option, particularly for small-scale forest growers in the North Island of New Zealand. We evaluated the profitability of conceptual CCF regimes using two case study forests: Blue Mountain (109 ha, Taranaki Region, New Zealand) and Spring Creek (467 ha, Manawatu-Whanganui Region, New Zealand). We ran a strategic harvest scheduling model for both properties and used its results to guide a tactical-spatially explicit model harvesting small 0.7 ha units over a period that spanned 35 to 95 years after planting. The internal rates of return (IRRs) were 9.16 and 10.40% for Blue Mountain and Spring Creek, respectively, exceeding those considered robust for other forest species in New Zealand. The study showed that small owners could benefit from carbon revenue during the first 35 years after planting and then switch to a steady annual income from timber, maintaining a relatively constant carbon stock under a continuous cover forestry regime. Implementing adjacency constraints with a minimum green-up period of five years proved feasible. Although small coupes posed operational problems, which were linked to roading and harvesting, these issues were not insurmountable and could be managed with appropriate operational planning. Full article

The production of iron and steel plays a significant role in the anthropogenic carbon footprint, accounting for 7% of global GHG emissions. In the context of CO₂ mitigation, the steelmaking industry is looking to potentially replace traditional carbon-based ironmaking processes with hydrogen-based direct reduction of iron ore in shaft furnaces. Before industrialization, detailed modeling and parametric studies were needed to determine the proper operating parameters of this promising technology. The modeling approach selected here was to complement REDUCTOR, a detailed finite-volume model of the shaft furnace, which can simulate the gas and solid flows, heat transfers and reaction kinetics throughout the reactor, with an extension that describes the whole gas circuit of the direct reduction plant, including the top gas recycling set up and the fresh hydrogen production. Innovative strategies (such as the redirection of part of the bustle gas to a cooling inlet, the use of high nitrogen content in the gas, and the introduction of a hot solid burden) were investigated, and their effects on furnace operation (gas utilization degree and total energy consumption) were studied with a constant metallization target of 94%. It has also been demonstrated that complete metallization can be achieved at little expense. These strategies can improve the thermochemical state of the furnace and lead to different energy requirements. Full article

This paper presents a stochastic analysis of a two-unit cold standby system incorporating imperfect switching mechanisms. Each unit operates in one of three states: normal, partial failure, or total failure. Employing Markov processes, the study evaluates system reliability by examining the mean time to failure (MTTF) and steady-state availability metrics. Failure and repair times are assumed to follow exponential distributions, while the switching mechanism is modeled as either perfect or imperfect. The results highlight the significant influence of switching reliability on both MTTF and system availability. This analysis is crucial for optimizing the performance of complex systems, such as thermal power plants, where continuous and reliable operation is imperative. The study also aligns with recent research trends emphasizing the integration of preventive maintenance and advanced reliability modeling approaches to enhance overall system resilience. Full article

Our regression analysis documents that energy policies to promote renewable energy development, as well as hydroelectric imports from Canada, lead to short-run reductions in average electricity prices (also known as merit-order effects) throughout the Northeast United States. Changes in the reliance upon renewable energy in one of the Northeast’s three interconnected electricity markets will impact wholesale prices in the other two. The retirement of a 1000 MW nuclear plant can increase prices by about 9% in the Independent System Operator of New England market and 7% in the New York Independent System Operator market in the short run at reference hubs, while also raising prices in neighboring markets. Some proposed large-scale off-shore wind farms would not only lower prices in local markets at the reference hubs modeled but would also lower prices in neighboring markets. Full article

Under the dual-carbon goals, with the rapid increase in the proportion of fluctuating power sources such as wind and solar energy, the regulatory capacity of traditional thermal power generation can no longer meet the demand for intra-day fluctuations. There is an urgent need to tap into the potential of flexible load-side regulatory resources. To this end, this paper proposes a low-carbon economic optimal dispatching strategy for virtual power plants (VPPs), considering the aggregation of diverse flexible and adjustable resources with the integration of wind and solar power. Firstly, the method establishes mathematical models by analyzing the dynamic response characteristics and flexibility regulation boundaries of adjustable resources such as photovoltaic (PV) systems, wind power, energy storage, charging piles, interruptible loads, and air conditioners. Subsequently, considering the aforementioned diverse adjustable resources and aggregating them into a VPP, a low-carbon economic optimal dispatching model for the VPP is constructed with the objective of minimizing the total system operating costs and carbon costs. To address the issue of slow convergence rates in solving high-dimensional state variable optimization problems with the traditional plant growth simulation algorithm, this paper proposes an improved plant growth simulation algorithm through elite selection strategies for growth points and multi-base point parallel optimization strategies. The improved algorithm is then utilized to solve the proposed low-carbon economic optimal dispatching model for the VPP, aggregating diverse adjustable resources. Simulations conducted on an actual VPP platform demonstrate that the proposed method can effectively coordinate diverse load-side adjustable resources and achieve economically low-carbon dispatching, providing theoretical support for the optimal aggregation of diverse flexible resources in new power systems. Full article

The operational efficiency of the main steam pipelines at thermal power plants is reduced due to several factors, including operating temperature, pressure, service life, and the frequency of process shutdowns, which contribute to the degradation of heat-resistant steels. The study aims to identify the features of changes in the sizes of grains and carbides along their boundaries, as well as mechanical properties (hardness, strength, plasticity and fracture toughness) along the wall thickness of both pipes in the initial state and after operation with block shutdowns. Preliminary electrolytic hydrogenation of specimens (before tensile tests in air) showed even more clearly the negative consequences of operational degradation of steel. The degradation of steel was also assessed using fracture toughness (J_IC). The value of J_IC for operated steel with a smaller number of shutdowns decreased by 32–33%, whereas with a larger number of shutdowns, its decrease in the vicinity of the outer and inner surfaces of the pipe reached 65 and 61%, respectively. Fractographic signs of more intense degradation of steel after a greater number of shutdowns were manifested at the stage of spontaneous fracture of specimens by changing the mechanism from transgranular cleavage to intergranular, which indicated a decrease in the cohesive strength of grain boundaries. Full article

The Adaptive Cross Ivy (ACIVY) algorithm is a novel bio-inspired metaheuristic that emulates ivy plant growth behaviors for complex optimization problems. While the original Ivy Optimization Algorithm (IVYA) demonstrates a competitive performance, it suffers from limited inter-individual information exchange, inadequate directional guidance for local optima escape, and abrupt exploration–exploitation transitions. To address these limitations, ACIVY integrates three strategic enhancements: the crisscross strategy, enabling horizontal and vertical crossover operations for improved population diversity; the LightTrack strategy, incorporating positional memory and repulsion mechanisms for effective local optima escape; and the Top-Guided Adaptive Mutation strategy, implementing ranking-based mutation with dynamic selection pools for smooth exploration–exploitation balance. Comprehensive evaluations on the CEC2017 and CEC2022 benchmark suites demonstrate ACIVY’s superior performance against state-of-the-art algorithms across unimodal, multimodal, hybrid, and composite functions. ACIVY achieved outstanding average rankings of 1.25 (CEC2022) and 1.41 (CEC2017 50D), with statistical significance confirmed through Wilcoxon tests. Practical applications in engineering design optimization and UAV path planning further validate ACIVY’s robust performance, consistently delivering optimal solutions across diverse real-world scenarios. The algorithm’s exceptional convergence precision, solution reliability, and computational efficiency establish it as a powerful tool for challenging optimization problems requiring both accuracy and consistency. Full article

With water availability being one of the world’s major challenges, this study aims to propose a Zero Liquid Discharge (ZLD) system for treating saline effluents from an urban wastewater treatment plant (UWWTP), thereby supplementing into the existing water cycle. The system, which employs membrane (nanofiltration and reverse osmosis) and thermal technologies (multi-effect distillation evaporator and vacuum crystallizer), has been installed and operated in Cyprus at Larnaca’s WWTP, for the desalination of the tertiary treated water, producing high-quality reclaimed water. The nanofiltration (NF) unit at the plant operated with an inflow concentration ranging from 2500 to 3000 ppm. The performance of the installed NF90-4040 membranes was evaluated based on permeability and flux. Among two NF operation series, the second—operating at 75–85% recovery and 2500 mg/L TDS—showed improved membrane performance, with stable permeability (7.32 × 10⁻¹⁰ to 7.77 × 10⁻¹⁰ m·s⁻¹·Pa⁻¹) and flux (6.34 × 10⁻⁴ to 6.67 × 10⁻⁴ m/s). The optimal NF operating rate was 75% recovery, which achieved high divalent ion rejection (more than 99.5%). The reverse osmosis (RO) unit operated in a two-pass configuration, achieving water recoveries of 90–94% in the first pass and 76–84% in the second. This setup resulted in high rejection rates of approximately 99.99% for all major ions (Cl⁻, Na⁺, Ca²⁺, and Mg²⁺), reducing the permeate total dissolved solids (TDS) to below 35 mg/L. The installed multi-effect distillation (MED) unit operated under vacuum and under various inflow and steady-state conditions, achieving over 60% water recovery and producing high-quality distillate water (TDS < 12 mg/L). The vacuum crystallizer (VC) further concentrated the MED concentrate stream (MEDC) and the NF concentrate stream (NFC) flows, resulting in distilled water and recovered salts. The MEDC process produced salts with a purity of up to 81% NaCl., while the NFC stream produced mixed salts containing approximately 46% calcium salts (mainly as sulfates and chlorides), 13% magnesium salts (mainly as sulfates and chlorides), and 38% sodium salts. Overall, the ZLD system consumed 12 kWh/m³, with thermal units accounting for around 86% of this usage. The RO unit proved to be the most energy-efficient component, contributing 71% of the total water recovery. Full article

Based on the sewage pipe network system in the service area of Qianshan-Gongbei Plant in Zhuhai City, the characteristics of water quality and quantity were analyzed, and the common problems were diagnosed. Through the establishment of a hydraulic-water quality model, the flow state of sewage in the pipe network is simulated, and the actual data is checked. It is found that there are significant differences in the quantity and quality of sewage pipe network systems in different seasons and land use types, and there is an obvious seawater backflow phenomenon in coastal areas. To solve these problems, this paper puts forward a series of optimization suggestions to improve the operation efficiency of sewage treatment plants and the reliability of urban drainage systems. Full article