• Energy Research

Water engineering problems are typically nonlinear, multivariable, and multimodal optimization problems. Accurate water engineering problem optimization helps predict these systems’ performance. This paper proposes a novel optimization algorithm named enhanced multioperator Runge–Kutta optimization (EMRUN) to accurately solve different types of water engineering problems. The EMRUN’s novelty is focused mainly on enhancing the exploration stage, utilizing the Runge–Kutta search mechanism (RK-SM), the covariance matrix adaptation evolution strategy (CMA-ES) techniques, and improving the exploitation stage by using the enhanced solution quality (IESQ) and sequential quadratic programming (SQP) methods. In addition to that, adaptive parameters were included to improve the stability of these two stages. The superior performance of EMRUN is initially tested against a set of CEC-17 benchmark functions. Afterward, the proposed algorithm extracts parameters from an eight-parameter Muskingum model. Finally, the EMRUM is applied to a practical hydropower multireservoir system. The experimental findings show that EMRUN performs much better than advanced optimization approaches. Furthermore, the EMRUN has demonstrated the ability to converge up to 99.99% of the global solution. According to the findings, the suggested method is a competitive algorithm that should be considered in optimizing water engineering problems.

Abstract Mono, hybrid, and ternary nanofluids were tested inside the plain and twisted-tape pipes using k-omega shear stress transport turbulence models. The Reynolds number was 5,000 ≤ Re ≤ 15,000, and thermophysical properties were calculated under the condition of 303 K. Single nanofluids (Al2O3/distilled water [DW], SiO2/DW, and ZnO/DW), hybrid nanofluids (SiO2 + Al2O3/DW, SiO2 + ZnO/DW, and ZnO + Al2O3/DW) in the mixture ratio of 80:20, and ternary nanofluids (SiO2 + Al2O3 + ZnO/DW) in the mixture ratio of 60:20:20 were estimated in different volumetric concentrations (1, 2, 3, and 4%). The twisted pipe had a higher outlet temperature than the plain pipe, while SiO2/DW had a lower T out value with 310.933 K (plain pipe) and 313.842 K (twisted pipe) at Re = 9,000. The thermal system gained better energy using ZnO/DW with 6178.060 W (plain pipe) and 8426.474 W (twisted pipe). Furthermore, using SiO2/DW at Re = 9,000, heat transfer improved by 18.017% (plain pipe) and 21.007% (twisted pipe). At Re = 900, the pressure in plain and twisted pipes employing SiO2/DW reduced by 167.114 and 166.994%, respectively. In general, the thermohydraulic performance of DW and nanofluids was superior to one. Meanwhile, with Re = 15,000, DW had a higher value of η Thermohydraulic = 1.678.

Significant wave height is an average of the largest ocean waves, which are important for renewable and sustainable energy resource generation. A large significant wave height can cause beach erosion, and marine navigation problems in a storm. A novel data decomposition based deep learning modelling framework has been proposed where Multivariate Variational Mode Decomposition (MVMD) is integrated with Gated Recurrent Unit (GRU) to design the MVMD-GRU model. First, a correlation matrix is established to identify statistically important predictor lags. Next, the MVMD is employed to decompose the predictor lags into intrinsic mode functions (IMFs). The GRU model is then applied to the IMFs as inputs to design the MVMD-GRU framework to forecast one-day ahead significant wave height. Several other benchmarking deep learning models were hybridized with MVMD for comparison purposes. The outcomes suggest that the hybrid MVMD-GRU achieved better accuracy using goodness-of-fit metrics for Hay Point, Townsville, and Gold Coast stations in Queensland, Australia. The results show that MVMD significantly improved the forecasting accuracy of the GRU model in terms of WIE = 0.983, 0.918, 0.983, NSE = 0.932, 0.735, 0.934, LME = 0.978, 0.758, 0.752 for Hay Point, Townsville, and Gold Coast stations. This work is valuable to monitor and manage clean energy resources to optimize sustained energy generation.

Cet article présente une nouvelle méthodologie optimale pour l'identification des paramètres d'une pile à combustible à membrane polymère de 50 kW (PEMFC) basée sur le modèle économique-fonctionnel. L'objectif de l'étude est d'estimer de manière optimale les paramètres du système de sorte que le coût total minimum ait été nécessaire pour la construction de la cheminée. Le coût total ici est la somme du coût de la pile à combustible et de ses auxiliaires en tenant compte du coefficient stœchiométrique de l'air et de l'hydrogène, de la pression du système, de la densité de courant et de la température du système. Pour résoudre le problème de minimisation, un modèle nouvellement modifié de l'algorithme d'optimisation de la racine fibreuse de l'herbe (MGRA) a été présenté. Les résultats finaux sont comparés à plusieurs algorithmes bien connus pour indiquer l'efficacité du système et la fiabilité du système vis-à-vis de différents paramètres a été indiquée en appliquant une analyse de sensibilité. Este documento presenta una nueva metodología óptima para la identificación de parámetros de una celda de combustible de membrana polimérica (PEMFC) de 50 kW basada en el modelo económico-funcional. El objetivo del estudio es la estimación óptima de los parámetros del sistema de modo que se haya necesitado el coste total mínimo para la construcción de la pila. El coste total aquí es la suma del coste de la pila de pilas de combustible y sus auxiliares considerando el coeficiente estequiométrico de aire e hidrógeno, la presión del sistema, la densidad de corriente y la temperatura del sistema. Para resolver el problema de minimización, se ha presentado un modelo recientemente modificado del Algoritmo de Optimización de Raíz Fibrosa de Hierba (MGRA). Los resultados finales se comparan con algunos algoritmos bien conocidos para indicar la eficiencia del sistema y la fiabilidad del sistema hacia diferentes parámetros se ha indicado mediante la aplicación de análisis de sensibilidad. This paper presents a new optimal methodology for parameter identification of a 50 kW polymer membrane fuel cell (PEMFC) based on the economical–functional model. The objective of the study is to optimal estimation of the system parameters such that the minimum total cost has been needed for the stack construction. The total cost here is the sum of the fuel cell stack cost and its auxiliaries by considering air and hydrogen stoichiometric coefficient, system pressure, the current density, and the system temperature. For solving the minimization problem, a newly modified model of the Grass Fibrous Root Optimization Algorithm (MGRA) has been presented. Final results are compared with some several well-known algorithms to indicate the system efficiency and the reliability of the system toward different parameters has been indicated by applying sensitivity analysis. تقدم هذه الورقة منهجية مثالية جديدة لتحديد المعلمات لخلية وقود غشاء البوليمر بقدرة 50 كيلو واط (PEMFC) بناءً على النموذج الاقتصادي الوظيفي. الهدف من الدراسة هو التقدير الأمثل لمعلمات النظام بحيث يكون الحد الأدنى للتكلفة الإجمالية مطلوبًا لبناء المكدس. التكلفة الإجمالية هنا هي مجموع تكلفة مكدس خلايا الوقود وملحقاته من خلال النظر في معامل التكافؤ للهواء والهيدروجين وضغط النظام وكثافة التيار ودرجة حرارة النظام. لحل مشكلة التقليل، تم تقديم نموذج معدل حديثًا لخوارزمية تحسين الجذور الليفية العشبية (MGRA). تتم مقارنة النتائج النهائية مع بعض الخوارزميات المعروفة للإشارة إلى كفاءة النظام وقد تمت الإشارة إلى موثوقية النظام تجاه المعلمات المختلفة من خلال تطبيق تحليل الحساسية.

The challenge to determine the best policies for hydropower multiple reservoir systems is a high-dimensional and nonlinear problem, making it challenging to attain a global solution. To efficiently optimize such a complicated solution, the creation of a high-precision optimization algorithm is critical. Hence, this research proposes a Multi-strategy Slime Mould Algorithm (MSMA) to determine the optimal operating rules for a complicated hydropower multiple reservoir prediction problem. The MSMA system proposed employs an effective wrap food mechanism to strengthen local and global capability; an enhanced solution quality (ESQ) to promote solution quality; and the interior-point method to implement an influential exploitation mechanism. The numerical testing of 23 test functions demonstrates the efficiency of the MSMA algorithm in solving global optimization issues. The newly developed method is then used to optimize the operation of a complex eight-reservoir hydropower system, with the proposed MSMA approach resulting in 0.999% of an ideal global solution, according to the optimal findings. The results of the multi-reservoir system show that proposed MSMA method was able to generate about 16.6% more power than the SMA. Consequently, the recommended method outperforms the other well-known optimization methods for maximizing power in the multi-reservoir system. Finally, this study also provides a useful tool for optimizing the complicated hydropower multiple reservoir problems.

AbstractCoastal vulnerability assessment is the key to coastal management and sustainable development. Sea level rise (SLR) and anthropogenic activities have triggered more extreme climatic events and made the coastal region vulnerable in recent decades. Many parts of the world also noticed increased sediment deposition, tidal effects, and changes in the shoreline. Farasan Island, located in the south-eastern part of Saudi Arabia, experienced changes in sediment deposition from the Red Sea in recent years. This study used Digital Shoreline Analysis System (DSAS) to delineate the shoreline changes of Farasan Island during 1975–2020. Multi-temporal Landsat data and DSAS were used for shoreline calculation based on endpoint rate (EPR) and linear regression. Results revealed an increase in vegetation area on the island by 17.18 km2 during 1975–1989 and then a decrease by 69.85 km2 during 1990–2020. The built-up land increased by 5.69 km2 over the study period to accommodate the population growth. The annual temperature showed an increase at a rate of 0.196 °C/year. The sea-level rise caused a shift in the island's shoreline and caused a reduction of land by 80.86 km2 during 1975–2020. The highly influenced areas by the environmental changes were the north, central, northwest, southwest, and northeast parts of the island. Urban expansion and sea-level rise gradually influence the island ecosystem, which needs proper attention, management, policies, and awareness planning to protect the environment of Farasan Island. Also, the study’s findings could help develop new strategies and plan climate change adaptation.

The total quantity of solar energy falling on a horizontal plane surface is the global solar exposure (GSE, i.e., total solar energy). Precise forecasting of GSE is important in many fields such as renewable energy, agriculture, and public health, particularly by the limited hydro-meteorological time series information. This research aims to develop an advanced multi-processing deep learning (DL) paradigm to forecast weekly GSE based on maximum (Tmax) and minimum (Tmin) air temperatures as the drivers at Brisbane and Perth airport stations in eastern and western Australia during 2000 to 2022. The proposed model was comprised of an extra tree feature selection (FS) integrated with two novel decomposition techniques, namely time-varying filtering-based empirical mode decomposition (TVF-EMD), and empirical wavelet transform (EWT), and a powerful ensemble deep random vector functional link (ED-RVFL) approach. To validate the main model, the RVFL, bidirectional long-short term memory (Bi-LSTM), and bagged regression tree (Bagging) machine learning (ML) models were examined in hybrid and standalone counterpart frameworks. First, the extra-tree FS determined the significant lags of the predictors based on an importance benchmark criterion. Then, by applying the optimal gained lags to the feeding models, all of the original predictors were decomposed using TVF-EMD and EWT univariate feature extraction. The final forecast was computed by aggregating all the individual forecasts of the intrinsic mode functions (IMFs) and residual components. In addition, eight statistical indicators (including coefficient of correlation: R, root mean square error: RMSE, Kling-Gupta efficiency: KGE, index of agreement: IA, uncertainty coefficient with 95% confidence level: U95%, mean absolute percent error: MAPE, Nash-Sutcliffe efficiency: NSE, and mean absolute error: MAE) and several graphical methods were utilized to evaluate the performances of the models. The modeling results indicated that the ED-RVFL-TVF-EMD (R = 0.9665, RMSE = ...

Global technological advancements drive daily energy consumption, generating additional carbon-induced climate challenges. Modifying process parameters, optimizing design, and employing high-performance working fluids are among the techniques offered by researchers for improving the thermal efficiency of heating and cooling systems. This study investigates the heat transfer enhancement of hybrid “Al2O3-Cu/water” nanofluids flowing in a two-dimensional channel with semicircle ribs. The novelty of this research is in employing semicircle ribs combined with hybrid nanofluids in turbulent flow regimes. A computer modeling approach using a finite volume approach with k-ω shear stress transport turbulence model was used in these simulations. Six cases with varying rib step heights and pitch gaps, with Re numbers ranging from 10,000 to 25,000, were explored for various volume concentrations of hybrid nanofluids Al2O3-Cu/water (0.33%, 0.75%, 1%, and 2%). The simulation results showed that the presence of ribs enhanced the heat transfer in the passage. The Nusselt number increased when the solid volume fraction of “Al2O3-Cu/water” hybrid nanofluids and the Re number increased. The Nu number reached its maximum value at a 2 percent solid volume fraction for a Reynolds number of 25,000. The local pressure coefficient also improved as the Re number and volume concentration of “Al2O3-Cu/water” hybrid nanofluids increased. The creation of recirculation zones after and before each rib was observed in the velocity and temperature contours. A higher number of ribs was also shown to result in a larger number of recirculation zones, increasing the thermal performance.