Showing papers in "Energy Conversion and Management in 2021"

TL;DR: In this paper, the authors examined to what extent the currently carbon-intensive hydrogen production in Europe could be replaced by water electrolysis using electricity from renewable energy resources (RES) such as solar photovoltaic, onshore/offshore wind and hydropower (green hydrogen).

TL;DR: In this paper, the potential, achievements, and associated challenges of ammonia for power generation, with a particular focus on ammonia fuelled solid oxide fuel cells (SOFCs), are examined, and a rational design of catalyst in terms of preparation method, support and promoter is needed to carve out a catalyst that is commercially reliable and affordable.

TL;DR: In this paper, a dual-purpose green energy storage system with the aim of power and potable water production is proposed and investigated from the thermodynamic and economic points of view, which is based on an innovative combination of compressed air energy storage with solar heliostat and multi-effect thermal vapor compression desalination units that provides power and clean water without any emissions.

TL;DR: The results in this paper show that the EMD method can improve the wind speed prediction performance when it is combined with LSTM and after decomposition, L STM is suitable for predicting high complexity subsequences and the ARIMA is suited for effectively predicting low complexity subsequence based on the different sample entropies.

TL;DR: In this article, an innovative liquid cooling plate (LCP) embedded with phase change material (PCM) is designed for electric vehicle (EV) battery thermal management, which takes advantage of both active (liquid) and passive cooling methods.

TL;DR: Various data decomposition techniques that can denoise the signal are investigated and applied to partition the input time series into several high and low-frequency signals and establish that the proposed EWT- based hybrid model outperforms other decomposition-based models in accuracy and stability.

TL;DR: In this article, a Stackelberg game-based optimization framework is proposed for the optimal scheduling of integrated demand response (IDR)-enabled integrated energy systems with uncertain renewable generations, where the IEO acts as the leader who pursues the maximization of his profits by setting energy prices, while the users are the follower who adjusts energy consumption plans to minimize their energy costs.

TL;DR: The proposed method outperformed the state-of-the-art deep learning architectures and demonstrated promising results for very short-term wind power forecasting due to its competitive performance.

TL;DR: A review of the use of ionic liquid (IL) catalyzed synthesis of biodiesel has become a promising pathway to an eco-friendly production route for biodiesel as discussed by the authors, where the authors have discussed the state of the art of using ILs along with the physicochemical properties of the produced biodiesel.

TL;DR: A novel discrete grey model with time-varying parameters is initially designed to deal with various PPG time series featured with nonlinearity, periodicity, and volatility, which widely exist in the long-term PPG sequences.

TL;DR: In this article, the progress of ammonia combustion technologies in spark ignition engines, compression ignition engine, and gas turbine was reviewed, and the results showed that aqueous ammonia elevates heat release rate of full load compression ignition engines by almost 10%.

TL;DR: In this paper, the authors investigated direct cooling performance characteristics of Li-ion battery and battery pack for electric vehicles using dielectric fluid immersion cooling (DFIC) technology, and the experimental results showed that Liion pouch cell immersed in flowing dielectoric fluid assisted with tab cooling showed better cooling performance with 46.8% reduction in the maximum temperature at the positive tab compared to natural convection at 3C discharge rate.

TL;DR: A state-of-the-art optimization method, namely, directional permutation differential evolution algorithm (DPDE), to tackle the parameter estimation of several kinds of solar PV models, and extensive comparative results show that DPDE outperforms its peers in terms of the solution accuracy.

TL;DR: A compact guide of existing model modification and novel model development regarding predicting global solar radiation, including input parameters, feature selection and model development for machine-learning models is provided.

TL;DR: In this article, the effects of using micro algae biodiesel in IC engines and its output characteristics for different micro- algae biodiesels have been highlighted and analyzed, and a technoeconomic analysis has been drawn.

TL;DR: In this paper, the authors explored the use of metasurface in designing an aerodynamic system for potential vortex-induced vibration suppression or energy harvesting, and found that the presence of meta-surface patterns has a significant influence on the aerodynamic characteristics of the bluff body.

TL;DR: A real-time cost-minimization energy management strategy to mitigate the vehicle’s operating cost is proposed via model predictive control, wherein both hydrogen consumption and energy source degradations are incorporated in the multi-objective cost function.

TL;DR: This study adopts a novel hybrid deep learning-based evolutionary approach in an attempt to improve the accuracy of wind speed prediction, consisting of a bidirectional long short-term memory neural network, an effective hierarchical evolutionary decomposition technique and an improved generalised normal distribution optimisation algorithm for hyper-parameter tuning.

TL;DR: Analytical results show that, when compared with the traditional mainstream models, the strategy of using the sequences provided by the signal decomposition technology as the input features can significantly improve the prediction accuracy.

TL;DR: Experimental and comparative results demonstrate that the MPPCEDE exhibits higher accuracy and reliability, and has fast convergence speed by comparing with several methods in extracting parameters of PV models.

TL;DR: An improved algorithm based on the new metaheuristic marine predators algorithm to extract the optimal values of photovoltaic parameters is proposed and the results show that the proposed algorithm is highly correlated with the measured current–voltage data so that it can offer a useful alternative for parameter estimation of photvoltaic models.

TL;DR: The analyzed results indicate that the intelligent techniques are the superior to the hybrid optimisation of multiple energy resources software tool in terms of costs and environmental point of view.

TL;DR: The results indicate that PEMFCs with optimized parameters perform better than the base model in terms of all three performance indexes, demonstrating the success of this approach in solving time-consuming multi-optimization problems.

TL;DR: In this paper, an economic and environmental assessment of zero liquid discharge (ZLD) desalination systems in the Eastern Mediterranean is presented, where two scenarios are considered: ZLD brackish water and ZLD seawater systems, both aimed at producing saleable freshwater and mixed solid salt.

TL;DR: The feasibility and effectiveness of the proposed ISSA based MPPT have been validated experimentally, and the results clearly demonstrate its capability in tracking the GMPP with an average efficiency of 99.48% and average tracking time of 0.66 s.

TL;DR: The results show that the case study which contains solar PV, DG, and battery energy storage (BES) was the best case in terms of economic, environmental, and social assessment.

TL;DR: This study demonstrates the deep learning technologies are powerful complements for optimizing the PEMFCs by designing a performance prediction method based on the random forest algorithm and convolutional neural networks, which can reduce unnecessary experiments for MEA development.

TL;DR: In this article, the pyrolysis-catalysis of different types of waste plastics was investigated in the presence of an Fe/Al2O3 catalyst, and the results showed that PP, HDPE, and LDPE showed higher H2-enriched gaseous product yields.

TL;DR: In this article, a comparative optimization analysis of a hydrogen-based proton exchange membrane (PEM) fuel cell integrated with an organic Rankine cycle (ORC) using twenty different zeotropic mixtures is accomplished.

TL;DR: In this article, a comprehensive review of the recovery and utilisation of waste-heat in complex or large industries is presented, which provides constructive guidelines for researchers and enterprises engaged in related fields.