Optimal System Performance Research Articles

Reversible solid oxide cells-based hydrogen energy storage system for renewable solar power plants

The power-H2-power system based on reversible solid oxide cell is a promising pathway for large-scale renewable energy storage but not well understood due to the absence of comprehensive system analyses. In this study, a reversible solid oxide cell-based H2 energy storage system for a 100 % renewable solar power plant is proposed and analyzed through detailed modeling approach and optimization framework. The detailed parametric analyses and economic evaluations are performed in electrolysis (12:00 pm, sufficient solar energy) and power generation (6:30 am, insufficient solar energy) conditions. Notably, it is found that larger stack capacity (Ncell) and higher operating temperature (TReSOC) of the cell enhances system net H2 production and system economics despite additional investment cost. Afterwards, the optimal system performance is obtained (VH2,produce = 498.40 Nm3·h−1, Z=263.59 $·h−1 for electrolysis, and VH2,consume = 380.33 Nm3·h−1 for power generation) through multi-objective optimization by fully considering cell internal operating characteristics and system energy-exergy-economic factors. Besides, it is also found that the performance of power-H2-power system is still limited by remarkable fuel (H2) costs for nighttime power generation (daytime H2 production is smaller than nighttime H2 consumption when Wtot = 1 MW). Therefore, the critical power capacity (Pcritical) is obtained, offering a convenient approach to determine the maximum output capacity (Pcritical = 880 kW) to make the power-H2-power system profitable at specific solar energy input. This study provides valuable insights between system capacity, economics, and cell operating features in solar power plants, which are useful for the design and optimization of practical power-H2-power systems for renewable energy storage.

Performance analysis and optimization of a zero-emission solar-driven hydrogen production system based on solar power tower plant and protonic ceramic electrolysis cells

The solar-driven high-temperature steam electrolysis is promising for efficient large-scale H2 production. In this study, a comprehensive component-to-system model and optimization framework is developed to investigate the performance of a zero-emission H2 production system based on solar power plant and protonic ceramic electrolysis cell. Compared to previous system studies, the detailed description of cell internal operating characteristics is realized by integrating multi-physics simulation and artificial neural network. After parametric analyses, it is found that the system energy/exergy efficiency and co-generation performance are complicated by each subsystem. And the optimal system performance (ηth = 50.63 %, Z = 179.63 $·h−1 and ηex = 33.03 %, Z = 178.94 $·h−1, with LCOE = 0.172 $·kWh−1 and ZH2 = 6.497 $·kg−1) is obtained considering cell operating features and system energy-exergy-economic factors through multi-objective optimizations. Besides, the tradeoff between system maximum H2 production capacity and cell internal thermal conditions is revealed. This study can facilitate the development of zero-emission green H2 production driven by renewable energy.

Stiffness optimization design of wheeled-legged rover integrating active and passive compliance capabilities

Compliance capability is one of the most important properties of suspension systems for rough-terrain robots. This imposes specific requirements on system stiffness design, which directly determines the platform's performance in response to external stimuli. To address the challenge of stiffness optimization design in active and passive compliant systems, this paper proposes a novel and practical method for optimizing stiffness for a terrain-adaptive wheel-legged rover. Firstly, the kinematic model of this multi-degree-of-freedom platform is established. Secondly, the deformation capability coefficient, load capacity coefficient, energy efficiency coefficient, and dynamic stability coefficient are derived as performance indices to assess the behavior of the system. By establishing the relationship between joint configuration variation and system stiffness, stiffness parameters could be evaluated through these performance indices. Ultimately, the global optimum stiffness parameters are selected from the refined intersection of the individual performance optimal domains. Thirdly, the optimum parameters are calculated and applicability verified numerically. The designed parameters are verified experimentally on a wheeled-legged rover. The experimental results demonstrate that the proposed algorithm can find the parameter combination that achieves optimal system performance, thereby enhancing the system's terrain adaptability.

Performance evaluation of ICX reactor in treatment of paper mill wastewater: a case study in South Vietnam.

This study evaluates the performance of the Internal Circulation eXperience (ICX) reactor in treating high-strength paper mill wastewater in the south of Vietnam. The ICX reactor effectively managed organic concentrations (sCOD) of up to 11,800 mg/L. Results indicate a volumetric loading rate (VLR) of 26.8 kg/m3 × day, achieving processing efficiency exceeding 81% while consistently maintaining volatile fatty acids (VFA) below 300 mg/L. The study employed Monod and Stover-Kincannon kinetic modeling, revealing dynamic parameters including Ks = 56.81 kg/m3, Y = 0.121 kgVSS/kgsCOD, Kd = 0.0242 1/day, μmax = 0.372 1/day, Umax = 151 kg/m3 × day, and KB = 175.92 kg/m3 × day, underscoring the ICX reactor's superior efficiency compared to alternative technologies. Notably, the reactor's heightened sensitivity to VFA levels necessitates influent concentrations below 1,400 mg/L for effective sludge treatment. Furthermore, the influence of calcium on treatment efficiency requires post-treatment alkalinity maintenance below 19 meq/L to stabilize MLVSS/MLSS concentration. Biogas production ranged from 0.6 to 0.7 Nm3 biogas/kg sCOD; however, calcium impact diminished this ratio, reducing overall treatment efficiency and biogas production. The study contributes valuable insights into anaerobic treatment processes for complex industrial wastewaters, emphasizing the significance of controlling VFA, calcium, and alkalinity for optimal system performance.

Low-diffraction EMI-shielding multiband optical window based on randomized metallic mesh

Electromagnetic interference (EMI) shielding optical windows are crucial for the optimal performance of electro-optical systems in environments exposed to electromagnetic radiation. Traditionally, metallic mesh structures have been favored as the optimal solution, offering high spectral transmittance coupled with efficient electromagnetic shielding. However, these conventional periodic meshes often lead to diffraction effects that can degrade image quality. In contrast, random structures partially homogenize high-order diffraction but lack thorough optimization. To address these challenges, we employ a novel optimization process to develop an innovative multiband optical window based on layered functional structures. This design employs a randomized metallic mesh structure, dramatically reducing higher-order diffraction optical energy by 74% compared to its periodic counterparts. Additionally, the device's EMI shielding effectiveness exceeds 20 dB in the 12–18 GHz frequency band. Moreover, a multiband antireflection coating comprising a 9-layer ZnS/YbF3 stack has been applied to minimize residual reflections achieving an average optical transmittance of 86.1% in the 0.4-0.7µm band, 89.8% at 1.064µm, and 81.1% in the 3-5µm band. We anticipate that our proposed multiband optical window will greatly enhance the application and effectiveness of EMI shielding in optical windows.

Sistem Informasi Penjualan dan Stok dilengkapi dengan Mapping Rak pada Toserba

In the digital era, the advancement of information technology is crucial for enhancing operational efficiency in retail businesses. This study aims to design and develop a sales and inventory information system with a shelf mapping feature for Toserba Decky, which faces challenges in managing stock and manual transactions. The methodology employed is the Waterfall approach, encompassing requirements analysis, design, implementation, and system testing. Data was collected through direct observation, literature review, and analysis of the store's shelf layout. The findings indicate that the developed system can improve data accuracy, reduce recording errors, and expedite product searching. The implications of this research suggest that an effective information system in managing transactions and inventory can enhance operational efficiency and customer satisfaction. This study recommends regular system updates and technical support to ensure optimal system performance and security.

Optimasi dan Modifikasi Debian untuk Meningkatkan Kinerja Sistem Operasi Real-Time

In today's digital era, operating systems play a crucial role in various technological devices. This paper discusses the optimization and modification of Debian, one of the most stable Linux distributions, to enhance its performance as a real-time operating system (RTOS). The primary objectives of this research are to improve the responsiveness, reliability, and stability of Debian in handling real-time tasks. The study involves modifying the kernel, adjusting scheduling settings, optimizing memory management, and integrating specific real-time software. The results of the tests show that the modified Debian provides lower and more stable task execution latency compared to the standard Debian. Tests were conducted using the cyclictest software to measure system latency and Htop to monitor CPU and memory performance. Additionally, the use of the VLC application as a real-world workload demonstrated that the optimized operating system could handle task priorities more efficiently, allocate resources as needed for real-time demands, and maintain stability under heavy workloads. This research significantly contributes to the development of Debian-based RTOS, which can be applied in fields such as industrial automation, robotics, and IoT devices. The optimizations ensure that the operating system can meet the high-performance and real-time reliability demands of these critical applications.

Thermodynamic performances of a novel multi-mode solar-thermal-assisted liquid carbon dioxide energy storage system

Liquid carbon dioxide energy storage is an efficient and environmentally friendly emerging technology with significant potential for integration with renewable energy sources. However, the heat recovery and utilization during compression and expansion are not implemented well. This paper proposes a multi-mode solar-thermal-assisted liquid carbon dioxide energy storage system integrated with the organic Rankine cycle. Three operation modes of normal operation mode, heat distribution operation mode, and solar thermal power generation mode are developed to achieve flexible control of the system under both normal and extreme environmental conditions. Thermodynamic performance analysis of the system under normal operation mode shows that compared to traditional system with energy storage density of 8.55 kWh/m3, the overall efficiency of the coupled system increases from 49.5 % to 62.1 %, with an energy storage density reaching 21.74 kWh/m3. The impact of key parameters such as temperature and pressure on system performance is discussed. Furthermore, an analysis of the heat flow distribution ratio under the heat distribution mode was conducted, indicating optimal system performance at a heat flow ratio of 6:4. Comparative analysis of the system performance under various operating conditions for the three operation modes was performed, elucidating the operational strategies of each mode.

Modified Whale Algorithm and Morley PSO-ML-Based Hyperparameter Optimization for Intrusion Detection

Intrusion detection averts a network from probable intrusions by inspecting network traffic to ensure its integrity, availability, and confidentiality. Though IDS seems to eliminate malicious traffic, intruders have endeavored to use different approaches for undertaking attacks. Hence, effective intrusion detection is vital to detect attacks. Concurrently, the evolvement of machine learning (ML), attacks could be identified by evaluating the patterns and learning from them. Considering this, conventional works have attempted to perform intrusion detection. Nevertheless, they lacked about high false alarm rate (FAR) and low accuracy rate due to inefficient feature selection. To resolve these existing pitfalls, this research proposed a modified whale algorithm (MWA) based on nonlinear information gain to select significant and relevant features. This algorithm assures huge initialization to improve local search ability as the agent’s positions are usually near the optimal solution. It is also utilized for an adaptive search for an optimal combination of features. Following this, the research proposes Morlet particle swarm optimization hyperparameter optimization (MPSO-HO) to improve the convergence rate of the algorithm by consenting it to produce from the local optimization by improving its capability. Standard metrics assess the proposed system to confirm the optimal performance of the proposed system. Outcomes explore the effective ability of the proposed system in intrusion detection.

Contribution of Glazed Balconies as a Passive Heating System in Contemporary Buildings in Northern Portugal

To mitigate greenhouse gas emissions responsible for global warming and climate change, governments have undertaken concerted efforts and established goals to restructure production and consumption patterns within the current global economy. The construction sector, which in Europe has significant energy use and related greenhouse gas emissions, recognizes adopting passive heating and cooling systems for buildings as a viable solution. The revival of vernacular passive solar strategies emerges as opportune within this context. Through dynamic simulations, this study aims to analyze and quantify the potential contribution of glazed balconies, a traditional passive heating system, to improve contemporary constructions’ thermal behavior and comfort conditions in mild temperate climates, such as in northern Portugal. Results indicate that this system can significantly enhance a building’s energy efficiency, reducing energy needs for heating and cooling by up to 47% while extending periods of thermal comfort indoors by nearly 900 h per year compared to buildings with non-glazed balconies. Proper use of natural ventilation and shading devices is essential to ensure optimal system performance and prevent overheating. This research underscores the potential of glazed balconies as a sustainable solution for enhancing contemporary buildings’ thermal-energy performance and comfort, contributing to the transition towards carbon-neutral constructions.

Advancing Water Deoiling Efficiency in Petroleum Production: A Comprehensive Case Study of Hybrid Flotation Technology Integration

_ The efficient management of increasing water cuts in oil fields is paramount for sustaining profitability and minimizing environmental impact. This article presents a comprehensive case study conducted by Saudi Aramco, focusing on the integration of hybrid flotation technology into a gravity water/oil separator (WOSEP) to enhance deoiling efficiency and reduce operational costs and carbon emissions. The retrofit involved merging enhanced gravity separation with induced gas flotation (IGF) within the same vessel, resulting in significant improvements in deoiling capacity, outlet oil-in-water content reduction, and operational efficiency. Detailed insights into the design, operation, and performance of hybrid flotation systems are provided, offering valuable guidance for similar initiatives in the petroleum industry. Introduction Petroleum production facilities face escalating challenges with increasing water-production rates during crude-oil extraction, particularly in waterflooding operations for secondary oil recovery. This trend not only escalates operational costs but also intensifies environmental concerns, particularly regarding energy consumption and carbon emissions associated with water-handling processes. In response to these challenges, Saudi Aramco embarked on a pioneering initiative to enhance water-deoiling efficiency by integrating hybrid flotation technology into a conventional gravity WOSEP. This case study presents a comprehensive analysis of the design, implementation, and outcomes of this transformative project, offering valuable insights for the broader petroleum industry. Methodology The upgrade process commenced with an assessment of the existing WOSEP system and its operational challenges. Recognizing the impending capacity constraints and escalating oil-in-water content, Saudi Aramco’s engineering team opted for a holistic approach that combined enhanced gravity separation with IGF within the same vessel. The retrofit involved reconfiguring the internal components of the WOSEP to accommodate the hybrid flotation system, including the installation of advanced plate-pack internals for gravity separation and the integration of IGF cells for additional oil removal. Rigorous testing and optimization procedures were conducted to ensure seamless integration and optimal performance of the hybrid system. Gas/Oil Separation Plant Process Description In a typical gas/oil separation plant, the initial phase involves the extraction of crude oil, associated gas, and produced water from wells via a production manifold. These constituents are then directed to a three-phase separator, also known as a high-pressure production trap (HPPT). In the HPPT, free water droplets are separated from the oil through gravity separation, aided by demulsifier injection at the production header. The separated free water is discharged to a WOSEP, typically a gravity separator, for deoiling.

Magnetohydrodynamic convection in a heat-generating ferrofluid within a corrugated cavity containing a rotating cylinder

This study introduces a novel approach by combining magnetohydrodynamic flow with Joule heating effects to investigate the conjugate mixed convective flow of ferrofluid in a non-homogenously warmed wavy-walled squared-shaped chamber with a spinning cylindrical object positioned at the center of the chamber. The current study seeks to maximize heat transmission effectiveness by scrutinizing optimum system attributes and conducting entropy production analysis. Numerical solutions are achieved by employing the Galerkin finite element weighted residual approach to solve the two-dimensional Navier–Stokes and heat energy equations representing the mathematical model. The parametric alterations encompass Grashof (103 ≤ Gr ≤ 106), Reynolds (31.62 ≤ Re ≤ 1000), and Hartmann (5.623 ≤ Ha ≤ 31.623) numbers, volumetric heat generation coefficient (0 ≤ Δ ≤ 10), thermal conductivity ratio (K = 20.07, 95.14), corrugation frequency (6.5 ≤ f ≤ 8.5), dimensionless corrugation amplitude (0.02 ≤ A ≤ 0.04), and dimensionless cylinder diameter (0.3 ≤ D ≤ 0.5). The study assesses the thermal characteristics of a heat source and the entropy generated within the computational domain while considering varying corrugation frequency and amplitude, cylinder diameter, thermal conductivity, strength of magnetism, and heat generation. The findings are quantitatively showcased through the Nusselt number of the hot wall, mean fluid temperature, overall entropy production, and thermal performance criterion (TPC) across the domain. After extensive analysis, it is evident that minimum cylinder diameter (= 0.3), corrugation frequency (= 6.5), and amplitude (= 0.02) while the maximum thermal conductivity ratio (= 95.14) ensure optimal system performance. Surprisingly, incorporating interior heat production diminishes thermal performance significantly while increasing TPC. Understanding the impacts of the magnetic field, Joule heating, and interior heat production on convective flow offers key perceptions into temperature variation, heat transport, velocity profile, and irreversible energy loss in numerous engineering applications.

An effective parametric sensitivity analysis to improve electrical and thermal energy/exergy efficiency of PVT system using nanofluid

A 3D steady-state model of a photovoltaic/Thermal system with nanofluid is studied to investigate the electrical and thermal performance of PVT by utilizing a combination of statistical modeling and numerical simulation. The 3D CAD (Computer Aided Design) modeling of the photovoltaic thermal (PV/T) is performed using Ansys Space Claim. The current study is based on using a 2-way coupling FSI approach for modeling nanofluid-based PVT systems. Five output responses electrical power, thermal power, electrical exergy, thermal exergy, and entropy generation are chosen against four input parameters, solar radiation (G_sun), ambient temperature (Tamb), mass flow rate (ṁ), and nanoparticles concentration % (φ) to analyze system performance. A statistical prediction model is developed using response surface methodology (RSM) that starts from the design of experiment (DOE). The predicted outcomes obtained from the statistical model and the numerical simulation of the heat transfer model exhibit the existence of good fitness. Furthermore, optimization is performed using RSM for various optimization objectives to suggest the ideal design parameter values for optimal system performance. Sensitivity analysis illustrates that a rise in ambient temperature causes a drop in electrical power production and leads to an increase in thermal power. However, the increase in solar radiation boosts electrical power and causes a drop in thermal exergy, entropy generation increases with an increase in solar irradiation. Similarly, an increase in mass flow rate leads to an increase in thermal power. Multi-objective optimization with the highest composite desirability values depicts the influence of input design parameters and has a favorable effect on the objective of concurrently maximizing electrical and thermal power.

Enhancing Energy Efficiency in Telehealth IoT through MultiObjective Optimization on a Hybrid Fog/Cloud Computing Platform

In the rapidly evolving field of Telehealth Internet of Things (IoT), the pursuit of energy-efficient solutions that coexist with optimal system performance is a critical concern. This paper introduces a novel approach to address this challenge by integrating multi-objective optimization techniques within a hybrid fog/ cloud computing platform. Building upon established research on a fog-based telehealth model, this study extends its investigation to encompass a broader spectrum of performance metrics, including energy efficiency, response time, throughput, and resource utilization. The study employs well-established multiobjective optimization algorithms, specifically NSGA-II (Non-dominated Sorting Genetic Algorithm II) and SPEA2 (Strength Pareto Evolutionary Algorithm 2), to construct a comprehensive optimization framework. An intricate objective function is meticulously formulated to quantify the trade-offs between energy efficiency and other key performance metrics, facilitating the identification of Pareto-optimal solutions. The resulting Pareto front offers illuminating insights into the nuanced interplay between energy efficiency and performance attributes, providing decision-makers with tailored options that cater to their specific priorities. Rigorous evaluation of these solutions through simulated experiments reveals a harmonious landscape where energy-saving imperatives coalesce harmoniously with response time, throughput, and resource utilization goals. The implications of this multi-objective optimization approach are analyzed in depth, underscoring its potential to reshape optimization paradigms for Telehealth IoT deployments within a fog/cloud hybrid platform. This research represents a pioneering stride towards reconciling energy efficiency and performance in Telehealth IoT systems, offering a nuanced perspective for informed decision-making and a sustainable future for energy-saving initiatives in Telehealth IoT applications

Patients’ satisfaction with services in HIV clinic at a public tertiary health institution in Ogun State, Nigeria: Patients-providers perspectives

Objective: This study assessed HIV patients´ satisfaction, factors associated with patients´ satisfaction and gaps in the quality of health care service delivery in a virology clinic in Ogun State, Nigeria. Methodology: A cross-sectional study design using mixed methods was used for the study. Three hundred and fifty adult HIV patients on HAART for uninterrupted six months were selected by multistage sampling technique. The Chi-square test was used to assess associations between categorical variables. The p-value used was 0.05. The key informants that were interviewed were the clinic manager, the clinic consultant, the adherence counsellor and the clinic pharmacist. Thematic analysis was done on the key informant interviews. Results: The mean satisfaction scores were high for confidentiality (4.24±0.97), overall care (4.17±0.56) and accessibility of care (4.09±0.82). Waiting time (mean of 3.35±0.42 minutes) and hospital environment (mean of 3.97±0.74) had the lowest mean satisfaction scores. Factors associated with satisfaction were patients’ income (p = 0.001), occupation (p = 0.001) and gender (p = 0.007). The key informants identified long waiting times in the clinic, shortage of manpower, inadequate financial support for patients and inadequate technical support for clinic activities as aspects of care for improvement. Conclusion: In this study, patients expressed a high level of satisfaction with the quality of care and the key informants identified challenges that affect optimal care in the clinic. Healthcare quality is a core dimension of health system performance. Continuous patient satisfaction and quality of care evaluations are needed for optimal health system performance.

Enhanced Adaptive Dynamic Surface Sliding Mode Control for Optimal Performance of Grid-Connected Photovoltaic Systems

This study presents an enhanced, adaptive, and dynamic surface sliding mode control (SMC), a cutting-edge method for improving grid-connected photovoltaic (PV) system performance. The suggested control approach uses dynamic SMC and adaptive approaches to enhance the robustness and efficiency of a system. Proportional–integral (PI) and SMC, two control systems for maximum power point tracking (MPPT) in PV systems, are compared in this paper. This study finds that the SMC system is a more effective and efficient MPPT approach for PV systems compared to the conventional PI control system. The SMC system’s unique feature is the capacity to stabilize grid voltage and attain a modulation index of less than one. An important component of power electronic system control is the index, which acts as a parameter representing the relationship between the output signal’s amplitude and the reference signal’s amplitude. The SMC method demonstrates improved robustness, efficiency, and stability, especially in dynamic operating settings with load and solar radiation changes. Compared to the PI control, the SMC exhibits a noteworthy 75% reduction in voltage fluctuations and an improvement in the power output of 5% to 10%. Regarding output power optimization, voltage stability, and accurate current tracking, the SMC system performs better than the PI control system. Furthermore, the SMC technique maintains a modulation index below one and guarantees grid voltage stability, both of which are essential for the efficiency and stability of power electrical systems.

Performance evaluation of microservices communication with REST, GraphQL, and gRPC

Microservice architecture has become the design paradigm for creating scalable and maintainable software systems. Selecting the proper communication protocol in microservices is critical to achieving optimal system performance. This study compares the performance of three commonly used API protocols: REST, GraphQL, and gRPC, in microservices architecture. In this study, we established three microservices implemented in three containers and each microservice contained a Redis and MySQL database. We evaluated the performance of these API protocols using two key performance metrics: response time and CPU Utilization. This study performs two distinct data retrieval: fetching flat data and fetching nested data, with a number of requests ranging from 100 to 500 requests. The experimental results indicate that gRPC has a faster response time, followed by REST and GraphQL. Moreover, GraphQL shows higher CPU Utilization compared to gRPC and REST. The experimental results provide insight for developers and architects seeking to optimize their microservices communication protocols for specific use cases and workloads.

Research on Optimization of Maintenance Task Scheduling for Metro Systems Based on Resource Constraints

Optimizing the maintenance scheduling of metro systems is a crucial task that necessitates meticulous coordination of labor, equipment, and workspaces to ensure optimal system performance and safety. A mathematical model and a two-stage teaching-learning-based optimization (TLBO)-resource operators crossover (ROC) algorithm are proposed aiming at optimizing the scheduling of maintenance tasks for metro systems. The mathematical model focuses on minimizing the makespan, which represents the total duration or time required to complete a set of tasks or activities within a project. In addition, it takes into account the need to balance the load on labor and workspaces, considering environmental constraints, limited resources, and strict scheduling requirements. A two-stage TLBO-ROC algorithm is specifically designed to enhance the scheduling process. It achieves this by iteratively updating the local best individual matrix, dividing it into groups, and adjusting the resource allocation. This algorithm effectively reduces the makespan while also achieving improved balance in the workspace load. The model and algorithm are tested on the Shenzhen metro system. Experimental results demonstrate that our proposed approach significantly reduces the makespan. In comparison to manual scheduling plans, the algorithm achieved a remarkable 28.06% reduction in the makespan. Moreover, when compared to benchmark algorithms, our proposed algorithm not only improves the makespan but also ensures more equitable occupation of workspaces by maintaining a similar balance in labor load.

Development of a poly-generation layout centered on the utilization of rice straw triggered by an air-gas turbine cycle; Multi-facet optimization

Multi-generation energy systems that rely on biomass present a hopeful answer to the challenge of sustainable energy production. The availability and accessibility of rice straw as an energy source make it a feasible source for clean energy production. Therefore, this study develops a model for a multi-generational energy system centered on the utilization of rice straw. This system produces fuel, power, heating, and freshwater triggered by an air-gas turbine cycle. To achieve optimal system performance, a response surface methodology is utilized for multi-objective optimization. The system's emission and efficiency are also taken into account during the assessment of its overall performance. This ensures that all factors are considered and the system performs at its best. Also, power production, air heating rate, hydrogen production, oxygen storage, and freshwater are also evaluated in details. The system successfully produced 2.08 kg/h and 16.5 kg/h biohydrogen and biooxygen, respectively. Surprisingly, the output power, produced fresh water, and hot air capacity were 283.9 kW, 159.2 l/h, and 4915 m3/h, respectively. It was turned out that the implementation of poly-generation framework triggered by rice straw is a strength and consistent scheme in terms of sustainability and efficiency.

A universal tool for estimating monthly solar radiation on tilted surfaces from horizontal measurements: A machine learning approach

In this study, a universal method for estimating monthly solar radiation on tilted surfaces from horizontal measurements was developed and validated, targeting the optimal design and performance of solar energy systems. Using data from the Photovoltaic Geographical Information System (PVGIS) across 740 global locations, three machine learning models—linear regression, random forest, and k-nearest neighbors (KNN)—along with a multilayer perceptron deep learning model, were evaluated. The dataset spanned tilt angles from 0° to 90° at 1° increments and 11,511,773 rows with six input features for predicting irradiance on any tilt angle. The KNN model was selected for further testing analysis due to its superior accuracy, achieving the lowest root mean square error (RMSE) of 1.42 kWh/m2 and mean absolute error (MAE) of 0.7 kWh/m2 on the validation dataset. Testing was conducted with 16 years of data from five locations not included in the training dataset, obtained from PVGIS and Solcast. Results show that the KNN model consistently outperformed the traditional isotropic model in four out of five cities using PVGIS data and in two using Solcast data, especially excelling in regions with stable climates and consistent irradiance profiles. Moreover, better prediction performance was observed with PVGIS data compared to Solcast data across all five cities, with PVGIS data yielding an RMSE of 3.03 kWh/m2 and an MAE of 2.41 kWh/m2, while Solcast data exhibited a higher RMSE of 5.22 kWh/m2 and MAE of 4.14 kWh/m2. Finally, these results advocate for a shift towards data-driven methodologies highlighting their enhanced reliability.