This study delineates the development of a solar energy system that leverages concentrated solar power (CSP) technology to supply both electricity and potable water for residential applications. The proposed thermal architecture uniquely integrates heliostat solar fields with a dual-loop power generation cycle, augmented by a seawater desalination system that employs reverse osmosis (RO) membranes. To bolster electricity production, a thermoelectric generator (TEG) has been incorporated into the system's design framework. A comprehensive analysis of the system has been performed, encompassing thermodynamic and economic evaluations. Furthermore, a parametric analysis has been executed to investigate the effects of critical parameters on the system’s operational efficiency. The efficacy of the system was rigorously assessed through a case study that examined its capabilities for daily production outputs. This research, grounded in the analytical projections from Saudi Arabia and the favorable environmental conditions characteristic of the region, explores the operational performance of the system within this specific geographical context. The primary objective of this inquiry is to determine the ideal operational parameters by employing multi-criteria optimization methods tailored to the established system. Variations in compressor pressure ratios were found to significantly affect the performance of the Brayton cycle and the exergetic efficiency of the system, with optimal economic efficiency being realized at a specific pressure ratio. Furthermore, increasing the inlet temperatures in the organic Rankine cycle has been shown to improve system efficiency up to a certain limit, beyond which potential reliability issues could arise. The case study demonstrated that electricity generation peaks during the summer months, particularly in June, aligning with a high volume of freshwater production totaling 264,530 m³. The optimization efforts achieved an exergetic efficiency of 17.69% and an overall cost of $359.58 per hour.

This article introduces a new advanced solution to act against global CO2 emission increment and reduce the building sector's primary energy needs and costs. The core idea is to harness domestic hot water's waste heat to improve the efficiency of heating, ventilation, and air conditioning systems. Also, the system is equipped with a naturally driven geothermal borehole to freely generate heating and cooling. The suggested system's performance is assessed and compared with the conventional model integrated with exhaust ventilation from all aspects using TRNSYS software. A genetic algorithm and artificial neural network model are combined to identify the optimal condition where energy cost is minimized, and the CO2 saving and energy reduction ratio are maximized. Moreover, the proposed system's performance is evaluated transiently by observing the impact of the outdoor condition on the main techno-environmental and economic aspects under optimal conditions. The findings indicate a conflicting shift in the main indicators, including emission and energy savings, levelized cost of energy, and saving ratio, when the key variables are altered, highlighting the optimization need. According to the results, the optimization cut the carbon emission by 600 kg and levelized energy cost by 14 $/MWh, resulting in a final value of 50.5 $/MWh. Additionally, the energy saving and saving ratio is improved by about 2.6 MWh and 1.2% over a year, showing the optimization robustness to find the best condition satisfying all metrics. The results present that under optimal conditions, integrating wastewater preheating and borehole backup for air preheating results in the highest heating contributions during January (14 MWh) and December (13 MWh).

Consumer electronics have transformed the way we interact with technology, improving convenience and connectivity in day-to-day lives. In the healthcare sector, recent technologies have resulted in enhanced diagnosis, treatment, and patient care. Wearables, artificial intelligence-based data analytics, and telemedicine transform the way of monitoring and managing health, fostering a proactive approach to well-being. The popularity of ChatGPT is proven great potential for AI-generated content (AIGC) that has formed a major impact on the artificial intelligence (AI) community and accelerates the reconsidering of the prospects of general AI. The AIGC is also exposed as a considerable scope to impulse healthcare electronics (HE). Although generative AI has achieved popularity like the formation of images, it could be employed for producing synthetic tabular information. The production of synthetic electronic health records (EHR) undertakes to increase the utilization of machine learning (ML) methods that commonly function with massive quantities of data. ML will identify non-intuitive classifier patterns that permit a new integration of patient feature predictive ability. Currently, deep learning (DL) techniques are effectively utilized in EHR data from medical domains. DL methods excellently captured the significant and beneficial features and patterns from the comprehensive medical information in EHR data. This study presents AI-generated content for Synthetic Electronic Health Record Generation with a Deep Learning-based Diagnosis (SEHRG-DLD) Model. The focus of the SEHRG-DLD technique is to initially generate the synthetic EHR data and then analyze the medical data for disease diagnosis using the DL model. The SEHRG-DLD technique comprises a two-stage process: synthetic data generation and disease diagnosis. At the initial stage, the SEHRG-DLD technique uses the ChatGPT tool to generate synthetic EHR data. Then, the SEHRG-DLD technique undergoes the disease diagnosis process using three sub-processes namely Harris Hawks Optimization (HHO) based feature selection, deep belief network (DBN) based classification, and Golden Jackal Optimization (GJO) based hyperparameter tuning. The application of the HHO and GJO algorithms helps in accomplishing enhanced diagnostic performance of the SEHRG-DLD technique. The performance analysis of the SEHRG-DLD technique is examined by employing the ChatGPT-generated dataset. The experimental results clearly stated the supremacy of the SEHRG-DLD technique over other recent methods for different measures.

This study delineates the development and evaluation of a power generation cycle characterized by the absence of carbon dioxide emissions, achieved through the direct combustion of oxygen and natural gas, a system commonly known as the Graz cycle. The analysis incorporates both thermodynamic and economic dimensions. In this system, hydrogen (H2) was initially produced from two separate sources to enable the conversion of carbon dioxide (CO2), sourced from the Graz cycle, into methane. The predominant method for hydrogen production is through the biomass gasification system, complemented by the use of hydrogen separation membranes. The solid oxide electrolyzer cell serves as the secondary source, wherein the necessary electrical energy is supplied by photovoltaic collectors. This research employs a robust methodological framework to undertake a comprehensive analysis of economic variables, with the objective of providing an accurate depiction of empirical conditions in the real world. This study conducts a comprehensive analysis of all relevant costs within the specified framework. A series of ANN-centered optimization analyses was performed to ascertain conditions that concurrently optimize the economic and thermodynamic dimensions of the system.

This study presents an innovative heat recovery methodology to enhance traditional combined power plants' technical and economic performance. The proposed system uses reverse osmosis (RO) for water treatment, lowtemperature gases for industrial cooling through an absorption refrigeration cycle (ARC), and proton exchange membrane (PEM) electrolysis for hydrogen generation. A comprehensive parametric investigation and a multicriteria optimization process employing artificial neural networks (ANNs) and genetic algorithms (GAs) are conducted. Under optimal conditions, this system can generate 67.5 MW of electrical power, 197.6 kg/h of hydrogen, 682 kg/s of fresh water, and 20.02 MW of cooling capacity. The operational cost is estimated at 6043 $/h, resulting in a levelized cost of electricity (LCOE) of 6.77 cents/kWh. The breakdown of costs shows that the RO unit accounts for 39.9 % of the total, with power generation units at 32.7 % and by-product modules at 27.4 %. Enhancing airflow leads to improved work output and cooling capacity without affecting exergy and cost metrics. Besides, increasing the compression ratio boosts fuel usage and overall system performance. Conversely, a higher middle temperature reduces outputs and exergy efficiency but lowers the total cost rate. The optimization results indicate a total cost rate reduction of 5171.5 $/h with an exergy efficiency of 36.94 %, and the use of ANNs has significantly cut optimization time from 94 h to just 9 min. Future research should focus on further optimizing system parameters and exploring integration with other renewable energy sources to boost sustainability and efficiency.

This comprehensive investigation undertakes a holistic examination of the design, simulation, and optimization of a hybrid thermal energy system (HTES) that synergistically integrates wind and solar energy sources for the simultaneous production of electricity, compressed hydrogen, and freshwater. This study introduces an innovative energy system design that integrates a supercritical CO2 Brayton cycle (SCO2-BC) with parabolic trough solar collectors (PTSCs) to increase efficiency and reliability. A key innovation is using waste heat from the SCO2-BC to power an organic Rankine cycle (ORC), which improves the performance and power generation capacity of the proposed system. Additionally, the machine learning optimization technique is employed to optimize the system, significantly reducing computational costs and runtime for the optimization process. The thermal energy input of HTES is supplied by PTSCs, which drive the SCO2-BC, while an ORC unit is employed to recuperate waste heat at the compressor inlet, thereby augmenting electricity generation. Furthermore, the HTES is augmented by a wind turbine to supplement power production. A multidisciplinary techno-economic and environmental framework was applied to analyze the performance of the proposed system. The preliminary simulation results indicate that the solar unit significantly contributes to both exergy destruction and the total cost rate, accounting for 53.8% of the total exergy losses and 64.9% of the total costs, respectively. Ultimately, the optimized simulation utilizing a hybrid machine learning approach achieved a peak exergy efficiency of 27.37% and a minimized total cost rate of 96.2 $/h. Under the optimal operating conditions derived from the multi-objective optimization, the levelized costs of the HTES’s products were determined to be 12.63 cents/kWh for electricity, 4.75 $/kg for compressed hydrogen, and 20.59 cents/m3 for freshwater. Furthermore, the environmental assessment indicated that the cost of reducing CO2 emissions is 3.69 $/h under optimal conditions.

Reducing carbon emissions is a vital approach to combat the global threat of climate change. As energy consumption continues to grow on a global scale, the shift towards renewable energy is crucial for maintaining sustainable development. Solar power, in particular, has emerged as a leading renewable resource due to its widespread availability and the potential to cover a significant portion of global energy demand. Nonetheless, the variability of solar energy poses challenges for ensuring a steady power supply. To overcome this, efficient energy storage systems, such as advanced batteries and thermal energy storage (TES) systems are essential. There is growing attention on solar energy storage, with a particular focus on phase change material (PCM) and TES systems. Here, a compact thermal energy storage (CTES) system with two heat transfer fluid plates and one rib- enhanced PCM plate was investigated to minimize the response time. RT42 was employed as the PCM within the plate. Selected for its suitable melting temperature range of 311.15-315.15 K, RT42 facilitates efficient thermal management, enabling effective storage and release of latent heat. Eight aluminum-made ribs were embedded to allow heat to penetrate deeper into the storage container. According to the several geometric parameters of the ribs such as angle of lower ribs, angle of upper ribs, and the distance between ribs, different configurations of ribbed CTES systems were introduced. Additionally, an artificial neural network-based anticipation model was introduced to predict system's melting performance, facilitating faster and more accurate optimization of design parameters. This innovative approach aids researchers in accelerating their future work on similar energy storage systems. Eventually, an optimal configuration (OC) was derived from the genetic algorithm and the anticipation model. Based on the results, the rib-less specimen took 19,648 s to melt completely, which was 118.2 % longer compared to the OC. This indicated a difference of nearly 3 h between the two systems, underscoring the effectiveness of the optimal configuration in conserving thermal energy throughout the day. Moreover, the rib- less system needed 130.5 % more time to melt 50 % of the PCM and 129.4 % more time to melt 80 % of the material. This stark difference further emphasized the efficiency of the OC in the entire stages of the charging in the CTES system. Among the ribbed specimens, there was a difference of about 41 min in the melting time, which highlights the importance of optimizing the geometric design in TES systems.

The present work introduces a new integrated system for higher penetration of renewable energy in local energy grids, flattening the peak load, dealing with worldwide energy demands, and slowing climate change by reducing carbon dioxide emissions. The idea involves solar and biomass combination through high-temperature parabolic solar collectors and a gasifier unit for clean heating production with minimal emission and the highest reliability. The system also has Rankine and Organic Rankine cycles for efficient power generation. Additionally, the surplus heat is exploited via a multi-stage flash desalination unit and absorption chiller for potable water and cooling generation with minimal cost thanks to the passive energy improvement method. In addition, proton exchange membrane electrolyzers are added for green hydrogen production from the surplus power. An in-depth thermodynamic, exergo-economic, and environmental assessment is conducted to evaluate the proposed renewable combination from all aspects using an engineering equation solver. Then, a multi-objective optimization method is implemented to find the most favorable operating condition in the MATLAB program. According to the results, chemical reactions, friction, and large temperature variations between the steam entering and leaving the gasifier unit are the main drivers of the energy system's exergy destruction. This is especially true in the gasifier unit. The gas turbine inlet temperature is vital for improving power generation and minimizing costs, according to the scatter distribution analysis of key design parameters. The results further show that energy and exergy efficiencies at the design condition are 39.5% and 28.1%. According to the results, the optimization improves the exergy efficiency and power production by 2.5% and 9000 kW, respectively. Finally, the optimum total cost and exergy destruction rates are 0.64 $/s and 85,496 kW, respectively.

To address the growing demand for sustainable energy solutions and the need for efficient utilization of resources, this study investigates the optimization of energy and exergy efficiencies in an integrated clean energy system using different machine learning algorithms. The analysis of variance (ANOVA) results demonstrated the significant impact of the utilization factor and temperature on system efficiencies, with the utilization factor showing a more pronounced effect. The single-objective optimization results revealed that decreasing the utilization factor significantly improves energy efficiency, while temperature has a minor influence. In the multiobjective optimization, the desired ranges for energy efficiency (62.5-63.1 %) and exergy efficiency (27.4-27.8 %) were set. The results identified an optimum point with a utilization factor of 0.763 and a temperature of 818.9 degrees C, achieving an energy efficiency of 63.02 % and an exergy efficiency of 27.77 %. These values fall within the desired ranges, confirming the effectiveness of the optimization approach by machine learning algorithms. The alignment between the machine learning predictions and thermodynamic modeling results further validated the accuracy and reliability of machine learning algorithms. The study highlights the importance of managing the utilization factor and temperature to optimize system efficiencies and provides a robust framework for future research and development in sustainable energy solutions.

This study introduces a novel integrated solar energy system planned to maximize energetic efficiency, reduce environmental concerns, and enhance economic viability. By incorporating multiple technologies, including PV/ T panels, an ORC, a TEG, an absorption chiller, and a PEM electrolyzer, this system offers a comprehensive solution for sustainable energy generation. Through optimization and analysis, significant performance improvements have been achieved. Exergy efficiency has increased from 19 % to 35 % by mitigating energy losses. Net power generation has also increased from 40 MW to 46 MW, boosting the system's energy output. The system's capability to reduce CO2 emissions by 10 % contributes to lowering climatic changes and allowing sustainable energy practices. Additionally, the LCOE has decreased from 10 cents/kWh to 6.2 cents/kWh, making the system more economically attractive. A case study in Saudi Arabia demonstrates the system's adaptability to diverse climatic conditions. The analysis explores how the system performs across different seasons, considering variations in temperature and solar irradiance. This comprehensive approach, combining multiple functionalities and addressing the challenges of diverse climates, positions this integrated solar energy system as a promising solution for sustainable energy production, particularly in regions with high solar irradiation.

Utilizing the capabilities of artificial intelligence can lead to the development of energy systems and power supply chain that are more efficient, sustainable, and resilient. The integration of machine learning techniques within these systems provides substantial benefits and is essential for enhancing overall performance. As the global community confronts challenges like climate change and rising energy demands, machine learning will play an increasingly vital role in defining the future of energy systems. This research examines how effective regression-based machine learning techniques are for analyzing and optimizing the performance of a geothermal combined heat and power system. It focuses on creating both linear and quadratic models to assess electricity generation, heat production, and the efficiency of the entire system. The evaluation of these models is performed through residual analysis and R-squared statistics. Results indicate that quadratic models surpass linear ones, with linear model achieving an R-squared value of 88.56 % for power generation, while the quadratic model reaches an impressive R-squared level of 99.88 %. Furthermore, the study demonstrates that quadratic machine learning models hold significant promise for optimizing system performance, shown by desirability metrics exceeding 0.99. This research highlights the importance of regression-based machine learning methods in analyzing and improving geothermal combined heat and power systems.

The proposed system uses a dual-loop organic Rankine cycle, a reverse osmosis desalination unit, an absorption cooling unit, and a thermoelectric generator to produce electricity and freshwater for urban areas. A thorough assessment of the system's thermodynamic and economic performance has been conducted, with a parameter-based investigation to assess the effect of key variables on the system performance. The parametric study indicates that rising the geothermal mass flow rate enhances the energy efficiency, but lowers the energy efficiency and affects the cooling requirements. Moreover, the optimum inlet temperature in turbine 1 increases the desalination efficiency up to 105.02 kg/s at 115 degrees C, and higher temperatures reduce the performance and system efficiency. Adjusting the temperature difference at the pinch point at Evaporator1 is crucial for system efficiency, with trade-offs between freshwater output, expenses, and exergy efficiency. The capability of the system to produce up to 6,048,000 L of potable water daily signifies a monumental leap towards meeting the water demands of nearly 42,000 individuals, based on European consumption standards. Lastly, the application of genetic algorithmsin the optimizationprocessresultsin an exergeticefficiencyof 32.79% and a cost rateof 58.05$/h, demonstratingthe system's enhancedoperationaleffectiveness

The use of biomass as a renewable source for biohydrogen production offers both environmental and economic advantages. A novel multi-generation system has been developed and modeled to generate biohydrogen, along with other energy outputs such as hydrogen storage, power, hot water, and hot air. This integrated system incorporates a gas turbine cycle, a proton exchange membrane, and a supercritical carbon dioxide Brayton cycle. After validating the model, the performance of the systems fueled by olive refuse and wheat straw biomasses has been evaluated. The system using wheat straw biomass produces more biohydrogen (39g/min compared to 33g/min), oxygen (307g/min compared to 260g/min), and power (316kW compared to 268kW). Conversely, the system using olive refuse biomass emits lower carbon dioxide (8.38g/kWmin compared to 8.94g/kWmin) and provides higher efficiency (76.8% compared to 65.9%). These findings demonstrate the versatility of the novel multi-generation system in harnessing different biomass types for biohydrogen production and other energy applications, while balancing environmental and economic considerations.

Chronic disease (CD) recognition involves identifying the existence or risk of CDs in individuals. CDs have chronic health illnesses categorized by slow progression and frequent reduction from intricate reasons. CDs comprise chronic respiratory diseases, heart disease, diabetes mellitus, and certain cancers. Earlier diagnosis is vital in handling CDs proficiently. Then, it permits lifestyle modifications, timely intervention, and medical services to avoid the progression of the disease and reduce its effect on their health. Recently, technical development, particularly in healthcare statistics and artificial intelligence (AI), has assisted in advancing sophisticated approaches and systems for CD recognition. These methodologies usually employ deep learning (DL) and machine learning (ML) models for investigating enormous databases, identifying patterns, and making predictions that rely on distinct health-related parameters. This study presents an accurate chronic disease detection and classification model using binary meta-heuristics with an ensemble deep learning (ACDDC-BMEDL) approach. The ACDDC-BMEDL methodology focuses on the procedure of average ensemble classifier with meta-heuristic-based feature selection (FS) and hyperparameter tuning processes. The ACDDC-BMEDL methodology uses a binary arithmetic optimization algorithm (BAOA) to choose better feature subsets. Additionally, the ACDDC-BMEDL methodology uses an average ensemble technique encompassing recurrent neural network (RNN), gated recurrent unit (GRU), and extreme learning machine (ELM) for classification procedure. The marine predator's algorithm (MPA) is employed for the hyperparameter tuning process. The experimental value of the ACDDC-BMEDL methodology was examined on 2 CD datasets. The performance validation of the ACDDC-BMEDL methodology portrays a superior value of 98.70% and 94.51% with recent methods concerning several metrics under Diabetes and HD datasets.

Remote patient monitoring has recently been popularised due to advanced technological innovations. The advent of the Sixth Generation Internet of Things (6G-IoT) communication technology, combined with deep learning algorithms, presents a groundbreaking opportunity for enhancing remote cardiac system monitoring. This paper proposes an innovative framework leveraging the ultra-reliable, low-latency communication capabilities of 6G-IoT to transmit real-time cardiac data from wearable devices directly to healthcare providers. Integrating deep learning models facilitates the accurate analysis and prediction of cardiac anomalies, significantly improving traditional monitoring systems. Our methodology involves the deployment of cutting-edge wearable sensors capable of capturing high-fidelity cardiac signals. These signals are transmitted via 6G-IoT networks, ensuring minimal delay and maximum reliability. Upon receiving the data, a densely connected deep neural network with an optimised swish activation function—designed explicitly for cardiac anomaly detection is employed to analyse the data in real-time. These algorithms are trained on vast datasets to recognise patterns indicative of potential cardiac issues, allowing immediate intervention when necessary. The proposed system’s efficacy is validated through extensive testing in simulated environments, demonstrating its ability to accurately detect and swiftly predict a wide range of cardiac conditions. Moreover, implementing 6G-IoT communication ensures the system's scalability and adaptability to future technological advancements.

The principal aim of this article is to optimize the thermal and electrical efficiency of a geothermal combined heat and power system through metaheuristic particle swarm optimization (PSO) method. The objective of this research is to conduct a thorough analysis of the incorporation of metaheuristic PSO technique, with a specific emphasis on the potential advantages and obstacles associated with the utilization of metaheuristic approaches in improving the effectiveness of geothermal energy systems. The utilization of a double-flash geothermal system in conjunction with a transcritical carbon dioxide Rankine cycle is utilized for the co-generation of electricity and thermal energy. The research utilized a PSO method to enhance power generation, heating capacity, and overall system efficiency. The PSO algorithm was employed to determine the optimum operational parameters for a pressure level of 820 kPa and a pressure ratio of 1.59, leading to the maximization of power output to 2591.4 kW The PSO algorithm effectively identified the optimal operational parameters as a pressure of 820 kPa and a pressure ratio of 1.59, resulting in the achievement of a peak power output of 2591.4 kW. The methodology has determined that a pressure of 916.4 kPa and a pressure ratio of 1.5 represent the optimal parameters for achieving a maximum heating capacity of 12329.1 kW.

Solar energy is critical to the global shift towards sustainable and low-carbon energy solutions. Unlike fossil fuels, solar energy produces no emissions during operation, making it a key driver of environmentally friendly power generation. However, the intermittency of solar power remains a challenge, necessitating efficient energy storage systems to ensure a steady supply. Thermal energy storage systems utilizing phase change materials (PCMs) offer a solution by storing excess solar energy and releasing it when needed. This study focuses on enhancing the charging capacity of the PCM within a novel triplex tube heat exchanger (TTHE). The design incorporates bionic- shaped fins to compensate for thermal conductivity within the PCM. Three artificial neural network (ANN) models were employed to estimate the melting duration for the PCM to attain liquid fractions of 0.5, 0.8, and 1. The expansion angle (B), initial length (D), and length of fin branches (Z) were systematically varied to investigate their influence on heat absorption. The ANN models exhibited high accuracy, with R2 values of 0.998 for the liquid fraction of 0.5, 0.998 for the liquid fraction of 0.8, and 0.996 for the liquid fraction of 1, demonstrating their precise predictive capability. The results revealed that B significantly reduced melting time, while Z consistently shortened the duration and eventually became the dominant factor. However, an excessive increase in these geometric parameters led to a counterproductive rise in the total melting time. Through optimization using a genetic algorithm, three optimal designs (design 1 (D1), design 2 (D2), and design 3 (D3)) were proposed. Compared to the fin-less TTHE, these designs reduced the full melting time by 71.58 %, 73.41 %, and 73.54 %, respectively. Faster melting improves the system's ability to store and release thermal energy more quickly, enabling more efficient utilization of solar power within the limited period of sunlight availability (usually 4-6 ha day). These findings underscore the potential of ANN-based approaches in optimizing the efficiency of solar energy storage systems.

This research investigates the thermohydraulic performance and exergy destruction associated with the flow of supercritical carbon dioxide (sCO(2)) within spirally coiled mini tubes. The study examines the impact of different cross-sectional geometries. The primary objective of the study is to examine the impact of critical parameters, including shape, hydraulic diameter, inlet temperature, mass flux, and operating pressure, on important variables such as friction factor, heat transfer coefficient, and exergy efficiency. The computational simulation employs the RNG k-epsilon model. The Coupled algorithm was utilized for the determination of velocity and pressure fields, utilizing second-order discretization for domain partitioning and first-order discretization for other terms. The carbon dioxide (CO2) was conceptualized as a compressible gas with complex thermophysical attributes that are contingent upon variations in temperature and pressure. The thermophysical properties of carbon dioxide are evaluated within a defined range of operating conditions (298. 15 K < T < 455 K and 8 MPa < p < 10 MPa). The observed trends in HTC (heat transfer coefficient) demonstrate a correlation with specific heat, showing a peak at lower temperatures under increased operating pressures. Elevated operational pressure results in a reduction of the maximum HTC. The augmentation of mass flux results in an increase in heat transfer coefficient, thereby indicating an improvement in system efficiency. An augmentation in hydraulic diameter yields diminished heat transfer coefficients, mitigated pressure loss, and heightened exergy destruction.

This study offers an in-depth thermodynamic analysis and optimization of an integrated renewable energy system that merges a double-flash geothermal system with a transcritical carbon dioxide Rankine cycle, utilizing machine learning algorithms. The innovative design aims to maximize the concurrent generation of heat and electricity, ultimately benefiting environmental sustainability and energy security. By employing regression machine learning algorithms, the research evaluates and enhances system performance, achieving remarkable R-squared accuracy levels of 98.86 % for heating output and 99.89 % for power output predictions. The thermodynamic modeling, which has been validated against recognized benchmarks, confirms the accuracy of the system's design. Optimization findings indicate that operating pressures between 840 and 870 kPa and pressure ratios of 1.56-1.60 deliver optimal outputs, with power production between 2582 and 2585 kW and heating output ranging from 12260 to 12280 kW. The system reaches its maximum performance at a pressure of 850 kPa and a pressure ratio of 1.57, resulting in a power output of 2583.97 kW and a heating output of 12279.3 kW. These results highlight the potential of combining advanced thermodynamic systems with machine learning methodologies to improve the efficiency and effectiveness of renewable energy sources.