Optimization and Validation of Solar Pump Performance by MATLAB Simulink and RSM

Singh, Vineet; Yadav, Vinod; Kumar, M.

doi:10.5109/6625723

Cited by 9 publications

(6 citation statements)

References 28 publications

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“…RSM is used for the optimization of experimental setup by mathematical regression analysis; it converts the response variables in the form of model equations in terms of input variables. George E. P. Box and Wilson used this method first time in 1951 46 , 47 …”

Section: Optimization Approach and Description Of The Modelmentioning

confidence: 99%

“…Response surface methodologyRSM is used for the optimization of experimental setup by mathematical regression analysis; it converts the response variables in the form of model equations in terms of input variables. George E. P. Box and Wilson used this method first time in 1951 46 ,47. The solar pump's total cost mainly depends on the discharge required by the crops and the head required to be developed by the pump.…”

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confidence: 99%

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Optimum design and analysis of solar pump with the help of genetic algorithm, a MATLAB tool, and RSM tool at minimum cost

Singh,

Yadav,

Kumar

et al. 2023

Env Prog and Sustain Energy

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In this research paper, a well‐performing solar pump at the minimum cost of project life is designed and installed. Initially, the capacity of submersible pumps and discharge have been determined based on the water requirement of the crop and the size of the field. The total number of solar panels, inverter size, and motor pump system have been designed based on the law of conservation of energy. The two optimization techniques genetic algorithm (GA) and response surface methodology (RSM) have been used for analyzing the pump performance parameters and total cost. The GA tool has been used to determine the optimum tilt angle on which responses namely solar flux, solar panel efficiency, exergy, and pump efficiency should be maximum. Moreover, the performance of the solar pump at the optimum tilt angle has been compared with the 28° fixed tilt angle (latitute of location). For the experimental study solar pump experimental setup of 5 hp power has been installed in Faculty of Engineering and Technology, MJP Rohilkhand University at 28.36°N, 79.43°E. The total cost of the solar pump has been optimized with the help of RSM based on the discharge and head of the solar pump. The new optimization approach reduced the levelized cost of electricity (LCOE) from 0.041 to 0.035 $/kWh and the payback period is reduced from 4 to 3 years. The annual increment in average solar flux and overall efficiency has been recorded to be from 606 to 697 W/m2 and 11% to 14% respectively.

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Section: Optimization Approach and Description Of The Modelmentioning

confidence: 99%

mentioning

confidence: 99%

Optimum design and analysis of solar pump with the help of genetic algorithm, a MATLAB tool, and RSM tool at minimum cost

Singh,

Yadav,

Kumar

et al. 2023

Env Prog and Sustain Energy

Self Cite

View full text Add to dashboard Cite

show abstract

“…In addition, the analysis of the simulation model results on the responses of floating crane barges without a module load is compared directly with the experimental results based on the basin test. The predictions of simulation models for solving research problems require direct verification with data from experimental or observational tests [20][21][22][23][24][25] . The characteristics of the RAO curve in the simulation model and ANSYS AQWA can be analyzed in more detail.…”

Section: Validationmentioning

confidence: 99%

Preliminary Modeling of Coupled Motion Response for Floating Crane and Suspended Module by Using Boundary Element Approach and Empirical Model

Firdaus,

Ali,

Hidayat

et al. 2023

Evergreen

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External forces and environments can destabilize the floating crane's performance and inflict a high risk of failure in operation. Considering this, the effect of the suspended module during the lifting operation in sea waves is investigated to evaluate the dynamic response of the floating crane. In this article, the combination of potential flow theory in the Boundary Element Method (BEM) and an empirical model is proposed for the prediction of coupled motion on the floating crane and suspended module. Calculations of multibody systems are solved using the coupled motion formula based on the frequency domain method. An analysis of the lifting operation under beam waves is examined with two variations in the weight of the suspended module. The motion characteristics are depicted with the response amplitude operator (RAO) on the nine rigid body degrees of freedom. The resonances of peak frequency occurred in the floating crane's responses due to the lifting load system. The peak frequency of the roll response on the floating crane shifted to a higher frequency due to the impact of the pendulum motion. The difference in peak response of roll motion between variations of suspended load is about 25.8 %. The acceleration response of the suspended module in the z-direction for the large module presented a lower peak frequency than the smaller module.

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“…In the context of Industry 4.0, the maintenance function is commonly referred to as Maintenance 4.0, where artificial intelligence plays a significant role in meeting industry requirements. Many domains, including artificial intelligence and control systems, can utilise fuzzy ideas 67) .…”

Section: Sustainable Maintenance With Industry 40mentioning

confidence: 99%

Artificial Intelligence as Key Enabler for Sustainable Maintenance in the Manufacturing Industry: Scope & Challenges

Saraswat,

Agrawal

2023

Evergreen

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Small and Medium-Enterprises (SMEs) in the manufacturing sector have developed a high level of dependability, accessibility, and viability in their frameworks as a result of rapid globalisation among the other industries. Maintenance management has a significant impact on the performance of the organization. Industries are looking to adopt sustainable maintenance practices to achieve the overall organizational performance. The use of Industry 4.0 technology increased the organization's productivity and improved the conventional maintenance plan. Due to technological advances, the era of Industry 4.0 with Artificial Intelligence (AI) and Machine Learning (ML), a subset of AI, has emerged as a tool for maintenance strategy. In this research, the role of AI as a enable technology for sustainable maintenance in manufacturing industries were examined through literature review process. The study concludes that the current trend in maintenance uses artificial intelligence and machine learning algorithms to detect equipment breakdowns for optimal efficiency of the plant. The future of predictive maintenance lies on AI and machine learning algorithms.

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Optimization and Validation of Solar Pump Performance by MATLAB Simulink and RSM

Cited by 9 publications

References 28 publications

Optimum design and analysis of solar pump with the help of genetic algorithm, a MATLAB tool, and RSM tool at minimum cost

Optimum design and analysis of solar pump with the help of genetic algorithm, a MATLAB tool, and RSM tool at minimum cost

Preliminary Modeling of Coupled Motion Response for Floating Crane and Suspended Module by Using Boundary Element Approach and Empirical Model

Artificial Intelligence as Key Enabler for Sustainable Maintenance in the Manufacturing Industry: Scope & Challenges

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