Cost Index Research Articles

To Compare the Effects of 4 Weeks Low Intensity Intermittent Training (LIIT) and Low Intensity Continuous Training (LICT) on Heart Rate Reserve, Heart Rate Recovery and Physiological Cost Index on College Going Girls

Background and Aim: Exercise strengthens heart muscle, improves blood flow, and lowers blood pressure. This translates to a healthier heart that pumps blood more effectively, recovers faster after exertion, uses oxygen better; reducing risk of heart disease. Exercise intensity is important aspect of enhancing health-related fitness. Aim of the study is to make appropriate prescription of the fitness, obesity, diseased person according to their needs and also see the impact of both type of training. Methods and Materials: Interventional study was conducted on girls of a physiotherapy college, Ahmedabad. Ethical approval was taken. 14 girls were randomly divided into 2 groups (LIIT and LICT), Protocol of 42 min was completed on treadmill for 4 weeks (5 days/week). Training was done at 50–70% maximal heart rate, 11-13 RPE with Talk test. In both group 5 min of warm-up and cool-down period was given. In LIIT, 4 sets with 4 minute of interval (32min), in LICT continuous walking/jogging (32 min) was done. Unpaired t test was applied between outcomes. Statistical analysis was done using SPSS. Result: 7 girls with mean age of (20.5714+1.718) years were included in intermittent training, 7 girls with mean age of (22=2.160) years were included in continuous training, there was significant difference between HRr but no significant difference between HRR and PCI of both the groups. Conclusion: Present study concluded that intermittent training is better than continuous training on heart rate reserve, heart rate recovery and physiological cost index on college going girls. Keywords: Low intensity intermittent training (LIIT), Low intensity continuous training (LICT), Heart rate reserve, Heart rate recovery, Physiological cost index, College going girls.

Machine-Learning Methods Estimating Flights’ Hidden Parameters for the Prediction of KPIs

Complex microscopic simulation models of strategic Air Traffic Management (ATM) performance assessment and decision-making are hindered by several factors. One of the most important is the existence of hidden parameters—such as aircraft take-off weight (TOW) and the selected cost index (CI)—which, if known, would allow for more effective performance modeling methodologies for assessing Key Performance Indicators (KPIs) at various levels of abstraction/detail, e.g., system-wide, or at the level of individual flights. This research proposes a data-driven methodology for the estimation of flights’ hidden parameters combining mechanistic and advanced Artificial Intelligence/Machine Learning (AI/ML) models. Aiming at microsimulation models, our goal is to study the effect of these estimations on the prediction of flights’ KPIs. In so doing, we propose a novel methodology according to which data-driven methods are trained given optimal trajectories (produced by mechanistic models) corresponding to known hidden parameter values, with the aim of predicting hidden parameters’ values of unseen trajectories. The results show that estimations of hidden parameters support the accurate prediction of KPIs regarding the efficiency of flights: fuel consumption, gate-to-gate time and distance flown.

Calculation of the Equal Education Cost Index in Indonesia: Using the Relative Importance Regression and Winsorization Approach

This study examines index calculations in determining the unit costs of equivalent education using an empirical approach. The composite index used in this study compares the Construction Cost Index (CCI) to Purchasing Power Parity (PPP). The current empirical literature requires further development on calculation and comparison methods for these two indices. The study employed data from the Indonesian Central Statistics Agency (CSA), including the Construction Cost Index (CCI) and Purchasing Power Parity (PPP) data from 2021, as well as data on the allocation of School Operational Assistance (SOA) funds for non-formal education in 2022. The results indicate that the weight value of CCI is more substantial than that of PPP. The multiple regression analysis conducted with a relative importance approach reveals a favourable correlation between CCI and PPP, indicating a strong interrelationship. Received: 2 August 2024 / Accepted: 30 October 2024 / Published: 05 November 2024

Design of a hybrid solar and biomass-based energy system integrated with near-zero energy building: Techno-environment investigation and multicriteria optimization

The current research aims to meet the energy needs of a group of residential buildings in Norway with the least amount of carbon dioxide emission and the greatest amount of renewable energy. The supply side based on the renewable renewable-based energy system is planned and scaled using the real-time data use of domestic hot water (DHW), heating and electricity necessary for the buildings. PVT panels are used in the hybrid solar and biomass-based energy systems to produce both the DHW requirements and the electricity production. The digester is used with the heat pump, which generates heat. A double-effect absorption refrigeration system is also utilized to provide the necessary cooling requirements. On the supply side, the management of the heat streams and the redirection of flows are done using a rule-based regulating system. The whole plant's size is supplied, and the dynamic energy simulation is run. The primary deciding criteria for heating the buildings are the costs of electricity and biomass. The whole system is then optimized depending on operating circumstances, and the outcomes are compared to the design point. PVT may be used to create more than 80% of the yearly DHW. Furthermore, summertime radiation accounts for 64.8% of cooling output since it is more intense and may be converted into cooling energy. Digester/CC also contributes 66.55% of the building's heating, showing that the designers should rely on biomass as wintertime energy costs are higher. The parametric study demonstrates that increasing PVT time and tank capacity has varied effects on efficiency and emissions. Also, On winter days, there was a significant drop in renewable energy generation from summer to autumn, from 82.7 MWh to 28.52 MWh. The optimization results show that at the TOPSIS point, the total cost, efficiency, and emission index are 9.73 $/hr, 36.8%, and 7.75kg/MWh, respectively.

Scheduling optimization of continuous climb and descent operations in busy terminal airspace

Based on the current terminal airspace structure, a method for scheduling aircraft arrival and departure that integrates trajectory optimization, conflict detection and multi-objective optimization is proposed to help implement continuous climb and descent operations in busy terminal airspace. First, based on the multi-stage optimal control theory, the Gaussian pseudo-spectral method is used to propose a vertical profile optimization method for continuous climb and descent operations, and the trajectory optimization of continuous climb and descent operations based on the cost index is realized. Secondly, according to the wake turbulence interval and release interval used by the runway, and the horizontal and vertical intervals of air operations, the Mahalanobis distance is used to establish an aircraft conflict detection model. Then, considering the demands of air traffic control, airlines, airports and other operating units, a multi-objective scheduling model and method for aircraft arrival and departure that can achieve the optimization results are proposed. Finally, two sets of arrival and departure data at Guangzhou Baiyun Airport during busy periods are selected, multiple interval parameters are set, alternative paths are introduced, and case analysis and comparative studies are carried out. The results show that during the busy period dominated by departures, the terminal airspace of Guangzhou Baiyun Airport can achieve continuous climb and descent operations during busy periods; during the busy period dominated by arrivals, two aircraft could not be dispatched. The introduction of alternative paths can reduce the number of aircraft that cannot be dispatched.

Comparing cadence-based energy expenditure and oxygen cost indices in able-bodied male people.

Walking is a fundamental aspect of daily life and exercise, with clinical benefits for cardiovascular health and muscle strength. However, accurately measuring energy efficiency during walking poses challenges due to equipment and spatial constraints. In this study, we proposed the cadence-based energy expenditure index (cEEI) and analyzed its correlation with the previously proposed index for measuring energy expenditure under various gait conditions. We enrolled 15 healthy participants and conducted an experimental protocol on a treadmill to measure the following energy expenditure-related indices: oxygen cost index (OCI), energy expenditure index (EEI), and cEEI. The participants underwent stages of walking at different speeds and inclinations that comply with the modified Bruce protocol while their heart rate, oxygen uptake, and cadence were recorded. Participants showed significant increases in heart rate, oxygen uptake, and cadence with higher walking speeds and inclinations. Correlation analysis revealed strong associations between cEEI and OCI, especially during walking conditions. Bland-Altman plots and interclass correlation coefficient analysis demonstrated a favorable agreement between cEEI and OCI, outperforming EEI. In conclusion, this study proposes cEEI as a reliable metric for estimating energy expenditure during walking by proving a strong correlation and agreement with OCI across various gait conditions. This suggests the potential for cEEI to provide real-time, individualized feedback on energy expenditure during walking, facilitating more personalized exercise prescriptions.

Forecasting Construction Cost Indices: Methods, Trends, and Influential Factors

The Construction Cost Index (CCI) is an important tool that is widely used in construction cost management to monitor cost fluctuations over time. Numerous studies have been conducted on CCI development and forecasting models, including time series, artificial intelligence, machine learning, and hybrid models. Therefore, this study seeks to reveal the complexity of CCI forecasting and identify the leading indicators, trends, and techniques for CCI prediction. A bibliometric analysis was conducted to explore the landscape in the CCI literature, focusing on co-occurrence, co-authorship, and citation analysis. These analyses revealed the frequent keywords, the most cited authors and documents, and the most productive countries. The research topics and clusters in the CCI forecasting process were presented, and directions for future research were suggested to enhance the prediction models. A case study was conducted to demonstrate the practical application of a forecasting model to validate its prediction reliability. Furthermore, this study emphasizes the need to integrate advanced technologies and sustainable practices into future CCI forecasting models. The findings are useful in enhancing the knowledge of CCI prediction techniques and serve as a base for future research in construction cost estimation.

A Real-Time and Online Dynamic Reconfiguration against Cyber-Attacks to Enhance Security and Cost-Efficiency in Smart Power Microgrids Using Deep Learning

Ensuring the secure and cost-effective operation of smart power microgrids has become a significant concern for managers and operators due to the escalating damage caused by natural phenomena and cyber-attacks. This paper presents a novel framework focused on the dynamic reconfiguration of multi-microgrids to enhance system’s security index, including stability, reliability, and operation costs. The framework incorporates distributed generation (DG) to address cyber-attacks that can lead to line outages or generation failures within the network. Additionally, this work considers the uncertainties and accessibility factors of power networks through a modified point prediction method, which was previously overlooked. To achieve the secure and cost-effective operation of smart power multi-microgrids, an optimization framework is developed as a multi-objective problem, where the states of switches and DG serve as independent parameters, while the dependent parameters consist of the operation cost and techno-security indexes. The multi-objective problem employs deep learning (DL) techniques, specifically based on long short-term memory (LSTM) and prediction intervals, to effectively detect false data injection attacks (FDIAs) on advanced metering infrastructures (AMIs). By incorporating a modified point prediction method, LSTM-based deep learning, and consideration of technical indexes and FDIA cyber-attacks, this framework aims to advance the security and reliability of smart power multi-microgrids. The effectiveness of this method was validated on a network of 118 buses. The results of the proposed approach demonstrate remarkable improvements over PSO, MOGA, ICA, and HHO algorithms in both technical and economic indicators.

The inertial-based gait normalcy index of dual task cost during turning quantifies gait automaticity improvement in early-stage Parkinson’s rehabilitation

BackgroundThe loss of gait automaticity is a key cause of motor deficits in Parkinson’s disease (PD) patients, even at the early stage of the disease. Action observation training (AOT) shows promise in enhancing gait automaticity. However, effective assessment methods are lacking. We aimed to propose a novel gait normalcy index based on dual task cost (NIDTC) and evaluate its validity and responsiveness for early-stage PD rehabilitation.MethodsThirty early-stage PD patients were recruited and randomly assigned to the AOT or active control (CON) group. The proposed NIDTC during straight walking and turning tasks and clinical scale scores were measured before and after 12 weeks of rehabilitation. The correlations between the NIDTCs and clinical scores were analyzed with Pearson correlation coefficient analysis to evaluate the construct validity. The rehabilitative changes were assessed using repeated-measures ANOVA, while the responsiveness of NIDTC was further compared by t tests.ResultsThe turning-based NIDTC was significantly correlated with multiple clinical scales. Significant group-time interactions were observed for the turning-based NIDTC (F = 4.669, p = 0.042), BBS (F = 6.050, p = 0.022) and PDQ-39 (F = 7.772, p = 0.011) tests. The turning-based NIDTC reflected different rehabilitation effects between the AOT and CON groups, with the largest effect size (p = 0.020, Cohen’s d = 0.933).ConclusionThe turning-based NIDTC exhibited the highest responsiveness for identifying gait automaticity improvement by providing a comprehensive representation of motor ability during dual tasks. It has great potential as a valid measure for early-stage PD diagnosis and rehabilitation assessment.Trial registration Chinese Clinical Trial Registry: ChiCTR2300067657

Sustainable and efficient separation of ternary multi-azeotropic mixture butanone/ethanol/water based on the intensified reactive extractive distillation: process design, multi-objective optimization, and multi-criteria decision-making

It is of great significance and necessity to develop a sustainable and efficient separation scheme of butanone (MEK), ethanol (EtOH), and water, which are usually formed during the cinepazide maleate synthesis process. To the best of our knowledge, the systematic separation of this ternary multi-azeotropic mixture remains comparatively less studied, compounded by the significant challenges posed by its intricate composition comprising four azeotropes and three distillation boundaries. Therefore, a systematic approach by integrating the process design, multi-objective optimization, and multi-criteria decision-making (MCDM) methods was proposed to achieve the sustainable separation. Initially, three separation schemes, i.e., triple-column extractive distillation (TCED), double-column reactive extractive distillation (DCRED), and side stream intensified double-column reactive extractive distillation (SS-DCRED), were mainly explored via the thermodynamic and kinetic analysis. Subsequently, the multi-objective particle swarm optimization (MOPSO) algorithm was employed to optimize the process based on three objectives namely the total annual cost (TAC), CO2 emissions, and process route index (PRI). Finally, an efficient MCDM method combining CRITIC and TOPSIS was employed to rank the Pareto front solutions of the corresponding process design. Based on the comparison results, the optimal TCED process exhibits the lowest PRI with a decrease of 50.61% and 55.06% compared to that of the SS-DCRED and the DCRED, respectively. It also shows the best performance simultaneously considering economic, environmental, and safety criteria. Of note, the SS-DCRED separation process exhibited the lowest TAC, with a decrease of 60.67% and 11.45% compared to that of the TCED and the DCRED, respectively.

Investigating the Effects of Exergame Training on Functional Activities Among Newly-Fitted Patients with Unilateral Transtibial Amputation: A Preliminary Study

Introduction: This preliminary study investigates the effects of adding exergame (EG) training to conventional exercises on the functional activities of newly fitted patients with unilateral transtibial amputation. Materials and Methods: A total of 22 newly fitted patients with unilateral transtibial amputation were equally and randomly divided into two groups as follows: the EG group who performed exercises and EG training and the control group who performed exercise only. The 2-min walk test, timed up and go test, amputee mobility predictor with prosthesis test, and physiological cost index were assessed at baseline and after 2 and 4 weeks of the intervention. Results: Both interventions effectively improved the 2-min walk test distance, timed up-and- go test, and the amputee mobility predictor with prosthesis test scores in patients with newly fitted transtibial amputation (P<0.001). Additionally, a significant improvement was observed in the timed up-and-go test time in the EG group versus the control group after the 4-week intervention (P=0.04, effect size=0.53). Conclusion: The findings of this preliminary study further support that adding EG to exercises significantly increases movement speed among amputee patients.

Development and modeling of power, hydrogen and freshwater production based on a novel double-flash geothermal power plant

In this article, a novel geothermal energy-assisted multigeneration plant is proposed and analyzed from the thermodynamic, economic, and environmental perspectives. In the design of the geothermal power system, a transcritical Rankine cycle, which employs CO2 as the working fluid (tCO2-RC), a Kalina cycle (KC), a hydrogen generation sub-plant, and a desalination unit are considered. The reverse osmosis (RO) desalination process is used for freshwater production, while the proton exchanger membrane (PEM) electrolyzer is utilized for hydrogen generation. In addition, parametric analyses are conducted to ascertain the impacts of the temperature of the geothermal source, mass flow rate, and flash chamber pressure on system performance. According to the results, the plant's overall energetic and exergetic efficiencies are determined to be 8.6 % and 34.9 %, respectively where the net power production is calculated as 3317.94 kW, and the system's total irreversibility is found to be 5852.88 kW. Furthermore, the hourly H2, O2, and freshwater generation amounts are 8.5 kg, 49.1 kg, and 9.3 m3, respectively. Finally, the levelized energy cost (LEC) and sustainability index of the system are found to be 0.128 USD/kWh and 0.215.

Development of a Prediction Model for Construction Project Cost in India: an Analytical Approach A Review

The fundamental problem in the construction industry is that building projects are completed attaken a tolls significantly higher than estimated venture taken a tolls consequently, it is fundamental todevelop a taken a toll forecast show that detain all components influencing the extend taken a toll bymeans of relapse examination. Development costs are continuously inclined to vacillations, with a longtermdesign of expanding, making the estimating prepare a difficult errand. Vacillations within the fetchedof building materials have a noteworthy affect on foreseeing the esteem of a venture and, as a result, onthe project's great conclusion It is well known that civil engineering projects are prone to cost overrunsand schedule delays. Lack of project scope and frequent changes leads to a negotiation process interms of cost and time between the first partyowner and the second partycontractors. In the project lifecycle, cost forecast is a precursor to budget prices and resource allocation, thus it is critical for anyorganization. The following objectives are framed based on the literature study identify the constructioncost predictability of various tools, identify the various influencing factors to predict the material price ofcement and steel, model the Construction Cost Index (CCI), predict the construction duration (Especiallyfor highway projects, as compared to other kind of projects. They are having more obstacles to completethe task), predict the land value, Regarding this issue this paper find the best methodology for thePrediction Model for Construction Project Cost by analysising the different researchers papersresearch.

Analysis of Infrastructure Spending, Construction Cost Index, Population Density on Road Stability

Analysis of Infrastructure Spending, Construction Cost Index, Population Density on Road Stability

Techno-Economic Assessment of Coaxial HTS HVAC Transmission Cables with Critical Current Grading between Phases Using the OSCaR Tool

In recent years, the scientific and industrial interest regarding alternative technologies for transmission cables has increased. These conductors should efficiently transmit significant amounts of power between grid nodes, which are expected to be particularly congested due to the projected global increase in electricity production. Superconducting cables are considered a promising solution in this context, offering the potential to transmit large amounts of energy with minimal losses and compact dimensions, thereby potentially benefiting the environment. To evaluate the feasibility of integrating superconducting cables into existing grids, techno-economic approaches should be adopted. Such techniques enable the conceptual design of a specific cable structure, allowing users to explore a wide range of operating parameters to derive optimal designs. This paper reports a comprehensive techno-economic analysis of High Voltage Alternating Current (HVAC) cables realized with High-Temperature Superconducting (HTS) tapes, with the aim to transmit extremely high-power level. The optimal coaxial design is selected using Optimization Tool for Superconducting Cable Research (OSCaR) by implementing a graded approach to the critical current of the HTS tapes used for the different phases. This optimization aims to achieve the most effective balance between the cost of the coated conductors and their electrical properties. The whole set of model equations, the user-defined parameters, and the applied constraints are detailed. The OSCaR tool is then applied to assess the impact on the optimized design of the cable system and the corresponding cost indexes of several crucial parameters, such as the maximum transmitted power, the voltage level, and the line length.

Multi-objective economic operation of smart distribution network with renewable-flexible virtual power plants considering voltage security index

This paper discusses the simultaneous management of active and reactive power of a flexible renewable energy-based virtual power plant placed in a smart distribution system, based on the economic, operational, and voltage security objectives of the distribution system operator. The formulated problem aims to specify the minimum weighted sum of energy cost, energy loss, and voltage security index, considering the optimal power flow model, voltage security formulation, and the operating model of the virtual power plant. The virtual unit includes renewable sources, like wind systems, photovoltaic, and bio-waste units. Flexibility resources include electric vehicle parking lot and price-based demand response. In the mentioned scheme, parameters of load, renewable sources, electric vehicles, and energy prices are uncertain. This paper utilizes the Unscented Transformation method for modeling uncertainties. Fuzzy decision-making is utilized to extract a compromised solution. The suggested approach innovatively considers the simultaneous management of active and reactive power of a virtual unit with electric vehicles and price-based demand response. This is performed to promote economic, operational, and network security objectives. According to numerical results, the approach with optimal power management of renewable virtual units is capable of boosting the economic, operation, and voltage security status of the network by approximately 43%, 47–62%, and 26.9%, respectively, to power flow studies. Only price-based demand response can improve the voltage security, operation, and economic states of the network by about 19.5%, 35–47%, and 44%, respectively, compared to the power flow model.

A preliminary exploration of waste red-bed as a supplementary cementitious material

A red-bed is a type of continental clastic sedimentary stratum, with red serving as the main color. This type of stratum accounts for 20% of the land area in China. In the process of infrastructure construction, a large amount of waste red-bed is generated. Waste red-bed exacerbates the shortage of land resources and is a misuse of resources. In this study, a preliminary exploration of the recycling of waste red-bed in central Yunnan Province was conducted. The studied red-bed contains approximately 30% clay minerals and was used as supplementary cementitious material (SCM) instead of clay after calcination. Through laboratory characterization, environmental impact assessment, and cost analyses, the feasibility of using the red-bed as a type of SCM was demonstrated. The results show that the dehydroxylation reaction of clay minerals in the red-bed was almost complete after calcination at 800 °C. A pozzolanic reaction occurred, and additional heat was released after the addition of the calcined red-bed (CRB) to the cement mortar. When the cement replacement ratio increased from 10% to 30%, the strength activity index decreased from 98.2% to 82.2%, which met the relevant requirements. However, compared with the control samples, the samples with replacement ratios of 40% and 50% had lower mortar fluidities by 57.3% and 75.5% and higher porosities by 33.3% and 20.0%, respectively. Owing to the dilution effect of the cement and the decrease in the mortar fluidity, the internal structure of the sample porosity increased, and the strength decreased dramatically. In addition, the environmental impacts, energy consumption, and cost of using waste red-bed as a SCM were quantitatively analyzed via a carbon emission index and a cost index.

Lower-Limb Exoskeletons for Gait Training in Parkinson's Disease: The State of the Art and Future Perspectives.

Gait dysfunction (GD) is a common impairment of Parkinson's disease (PD), which negatively impacts patients' quality of life. Among the most recent rehabilitation technologies, a lower-limb powered exoskeleton (LLEXO) arises as a useful instrument for gait training in several neurological conditions, including PD. However, some questions relating to methods of use, achievable results, and usefulness compared to traditional rehabilitation methodologies still require clear answers. Therefore, in this review, we aim to summarise and analyse all the studies that have applied an LLEXO to train gait in PD patients. Literature research on PubMed and Scopus retrieved five articles, comprising 46 PD participants stable on medications (age: 71.7 ± 3.7 years, 24 males, Hoehn and Yahr: 2.1 ± 0.6). Compared to traditional rehabilitation, low-profile lower-limb exoskeleton (lp-LLEXO) training brought major improvements towards walking capacity and gait speed, while there are no clear major benefits regarding the dual-task gait cost index and freezing of gait symptoms. Importantly, the results suggest that lp-LLEXO training is more beneficial for patients with an intermediate-to-severe level of disease severity (Hoehn and Yahr > 2.5). This review could provide a novel framework for implementing LLEXO in clinical practise, highlighting its benefits and limitations towards gait training.

Effect of three different exercise trainings on functional capacity in early stage severe burn patients: A randomized controlled trial.

This study investigated the effects of three different exercise protocols on functional capacity in early-stage burn patients. A total of 25 patients hospitalized in the Burn Center (wards and intensive care unit) were included in the study. The individuals were divided into three groups by covariate adaptive randomization according to burn percentage and type: 1 - standard treatment, 2 - standard treatment + aerobic exercise training, 3 - standard treatment + combined exercise (aerobic and resistance) determined by metabolic status. Individuals were evaluated weekly for six weeks from the first day of hospitalization using the 6-minute walk test, physiological cost index, and Medical Research Council muscle-strength measurements to assess functional capacity. A portable metabolism tracker device measured the metabolic status of all patients. Aerobic exercises and combined exercise (aerobic and resistance), when added to standard treatment and determined by metabolic status, were more effective in enhancing functional capacity than standard treatment alone (p<0.05). Patients performing the combined exercise (aerobic and resistance) showed faster improvement in functional capacity determined according to metabolic status than those in the other two groups (p<0.05). Aerobic exercises, when added to standard treatment and combined with aerobic and resistance exercises based on metabolic status, are more effective at improving functional capacity than standard treatment alone. Further controlled studies are required to explore the potential long-term benefits of this approach.

Using green hydrogen as the supplementary fuel for a scheme of power/fresh water production: An application of AI for optimal energy management and emission reduction

The primary objective of the current research is to speed up the shift towards a carbon-neutral economy and reduce our reliance on fossil fuels by developing a novel, highly efficient, environmentally friendly, and economically feasible multi-generation system based on solar-biomass hybridization. The essential element of this concept involves the introduction of green hydrogen into the combustion chamber, aiming to enhance the quality of the incineration products and facilitate the implementation of a waste-to-power Rankine cycle. The practicality of the suggested model is compared with a system that does not incorporate hydrogen injection. As a part of artificial intelligence, a neural network model integrated with the grey wolf optimizer is applied to ascertain the most optimal energy conversion/management condition with reduced computational time. The results show that the injection of additional hydrogen exhibits superior performance compared to the base system from all facets. The optimization achieves higher exergy efficiency and drinkable water production of 8.7 % and 27.9 kg/s while reducing the total cost and emission index of 13.7 $/h and 410.7 kg/GWh. The results indicate that the combustion chamber and steam generator are the poorest parts in terms of exergy under ideal conditions. Potential future directions of this work are to explore alternate techniques for generating hydrogen and perform regional evaluations to comprehend the accessibility and fluctuation of biomass and solar resources in various geographical areas.