Design of water‐energy management based on neural networks for an autonomous solar and wind renewable energy system coupled with brackish water desalination

Zgalmi, Abir; Cherif, Habib

doi:10.1002/ep.13929

Cited by 6 publications

(2 citation statements)

References 53 publications

(96 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Likewise, annual electrical energy (P m ) can be computed considering Equation (20). The value of net annual electrical energy (Ex e,annual ) is computed as:…”

Section: Estimation Of Yearly Overall Exergymentioning

confidence: 99%

“…Manchanda et al 18 studied tilted wick solar distillation-cum-dryer experimentally and reported that the cost of distillate was $0.03729/l. Manchanda and Kumar 19 investigated stepped solar still-cum-active dryer experimentally and concluded that the system performed better at lower value of mass flow rate _ m fr ð Þ. Zgalmi et al 20 designed water-energy management by incorporating artificial intelligence and solar desalting unit for getting highest possible freshwater yield for use.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Impact of variation in input parameters on energy matrices of dual slope active solar desalting unit

Krishna,

Singh,

Dwivedi

et al. 2024

Env Prog and Sustain Energy

View full text Add to dashboard Cite

This article presents the relationship between energy matrices of a dual slope solar desalting unit integrated with a number (N) of photovoltaic thermal flat plate collectors (NPVTFPC‐DSU) and the effect of variation in mass flow rate and N for a sustainable solar distillation. Although extensive research has been done on dual slope active solar desalting unit, however, the area of the impact of variation in and N on energy matrices of NPVTFPC‐DSU remains still untouched. This study is an attempt to bridge the gap that has been highlighted. An exhaustive analysis is performed considering all types of climatic situations of New Delhi, India. MATLAB is used to calculate the overall energy, exergy, and energy matrices for NPVTFPC‐DSU based on fundamental equations and relevant data as inputs. Concludingly, it is evident that there is a decrease in the value of energy payback period and life cycle conversion efficiency as the value of increases at a given value of N for ≤ 0.10 kg s−1. For kg s−1, the values remain constant. For NPVTFPC‐DSU, 6 and 10 are the best and most favorable values of N as far as energy‐based energy matrices and exergy‐based energy matrices are concerned.

show abstract

“…Likewise, annual electrical energy (P m ) can be computed considering Equation (20). The value of net annual electrical energy (Ex e,annual ) is computed as:…”

Section: Estimation Of Yearly Overall Exergymentioning

confidence: 99%

mentioning

confidence: 99%

Impact of variation in input parameters on energy matrices of dual slope active solar desalting unit

Krishna,

Singh,

Dwivedi

et al. 2024

Env Prog and Sustain Energy

View full text Add to dashboard Cite

show abstract

Comparative energy metrics and annual efficiency analyses of CPC‐ETC integrated single slope solar desalting unit

Singh,

Agarwal,

Singh

2024

Env Prog and Sustain Energy

View full text Add to dashboard Cite

This paper deals with the investigation of N identical compound parabolic concentrator evacuated tubular collector included single slope solar desalting unit (N‐CPC‐ETC‐SSU) by incorporating energy metrics for solving contemporary issue of water scarcity in the society. It will contribute to the sustainable development of society and recede the dependency on fossil fuels, too. The energy metrics investigation is important for energy system because it talks about the feasibility of the system. The methodology consists of getting fundamental equations from energy balance equations. All fundamental equations with relevant data are fed to the code developed in MATLAB followed by the computation of overall energy and exergy. All types of climatic situations have been considered for the analysis. Data required for the same are accessed from IMD, Pune, India. The energy payback period, energy production factor, and life cycle conversion efficiency for N‐CPC‐ETC‐SSU have been calculated at eight number of collectors, 0.012 mass flow rate and 0.14 m water depth. Results of N‐CPC‐ETC‐SSU have been compared with results of SSU included with ETCs. Concludingly, energy payback period is lower by 6.41%, energy production factor is higher by 6.25% and exergy‐based life cycle conversion efficiency is higher by 96.39% for N‐CPC‐ETC‐SSU than N‐ETC‐SSU.

show abstract

A Hybrid Deep Learning‐Based Power Management Strategy for PV‐Assisted Desalination Plant

Almashnowi,

AlGhamdi,

Alamier

et al. 2024

Part & Part Syst Charact

View full text Add to dashboard Cite

The increasing global need for freshwater has led to the widespread implementation of photovoltaic (PV) assisted desalination facilities as a viable and environmentally friendly remedy. The necessity for these plants resides in executing an effective power management strategy to provide dependable and economically feasible water generation. This paper utilizes a mixed architecture consisting of a convolutional neural network (CNN) and a deep Q‐learning Network (DQN) to implement a hybrid deep learning‐based power management strategy. The presented approach is modeled and executed in matrix laboratory (MATLAB) language, and the experimental findings validated that this algorithm achieved a computational time of 0.2 s and an energy loss of 0.01 megawatts, which is lower than the conventional models. Furthermore, the proposed strategy achieved a remarkable fit with an accuracy rate of 0.99, demonstrating its effectiveness in handling diverse load power and solar profiles in power management. Implementing this hybrid approach holds promise for a substantial reduction in operational expenditures and the advancement of sustainability in freshwater generation, making a valuable contribution toward a more environmentally friendly and optimized future for the desalination sector.

show abstract

Design of water‐energy management based on neural networks for an autonomous solar and wind renewable energy system coupled with brackish water desalination

Cited by 6 publications

References 53 publications

Impact of variation in input parameters on energy matrices of dual slope active solar desalting unit

Impact of variation in input parameters on energy matrices of dual slope active solar desalting unit

Comparative energy metrics and annual efficiency analyses of CPC‐ETC integrated single slope solar desalting unit

A Hybrid Deep Learning‐Based Power Management Strategy for PV‐Assisted Desalination Plant

Contact Info

Product

Resources

About