With rapidly increasing consumption of energy, shipping industry has imposed a huge burden on the marine environment. It is a general trend to increase the use of renewable energy on ships to improve the ship sustainability. This article summarized the current development and application of solar energy, wind energy and fuel cell in ship power systems. Furthermore, in order to investigate the advantages of sustainable design for the ships, for the first time, a hybrid PV, wind and fuel cell energy system was established for an oil tanker, and the economic and environmental analyses of the hybrid system were performed. The analysis results demonstrate that the optimal hybrid energy system can reduce 151,467kg emission of CO2 and provide 2.92% electricity for the ship gird per year.

Renewable energy storage and sustainable design of hybrid energy powered ships: A case study

Numerical analysis of porous flat plate solar collector under thermal radiation and hybrid nanoparticles using two-phase model

The current study aims to give insight into the usefulness of three underutilized yet exceptionally efficient machine learning approaches in estimating the specific heat capacity (SHC) of nanofluids: Gaussian process regression, extreme gradient boosting, and support vector machine. The study made use of three uncommon metal oxides-multiwall carbon nanotubes-water nanofluids. Several factors influencing the SHC of nanofluids were considered during model development, including volume concentrations (0.25 %–1.5 %), range of temperature (25–50 °C), the base fluid's SHC, the specific heat capacity of the nanofluid, the density of the base fluid, the density of the nanofluid, and the average diameter of the used nanoparticles. All three models predicted the SHC of hybrid nanofluids with high precision, suggesting that they are a viable alternative to the time-consuming, expensive, and complex experimental approaches for measuring the SHC of nanofluids. The extreme gradient boosting ( R = 0.9975, mean absolute percentage error = 0.01 %) model outperformed the Gaussian process regression ( R = 0.9973, mean absolute percentage error = 0.015 %) and support vector machines ( R = 0.9952, mean absolute percentage error = 0.021 %) in terms of prediction performance. The Nash-Sutcliffe efficiency for Gaussian process regression, extreme gradient boosting, and support vector machines models were 0.9956, 0.997, and 0.992, respectively, while Theil's U2 was 0.0752, 0.072, and 0.1015, indicating that the three machine learning algorithms performed well in the precise estimation of SHC of test nanofluids. These prognostic models can help to reduce the time and resources needed for experimental trials. • XGBoost, GPR, and SVM techniques were employed for modeling the SHC of three metal oxides-MWCNT-water nanofluids. • Best prognostic performance was achieved by the XGBoost model ( R = 0.9975, MAPE = 0.01 %). • Nash-Sutcliffe efficiency (NSCE) for XGBoost, GPR, and SVM models was 0.997, 0.9956, and 0.992. • Model's uncertainty was measured with Theil's U2 with high accuracy. 

Exploring the specific heat capacity of water-based hybrid nanofluids for solar energy applications: A comparative evaluation of modern ensemble machine learning techniques

Mohd Ridha Muhammad

Papers

Experimental investigations of the performance of a flat-plate solar collector using carbon and metal oxides based nanofluids