Preparation of Stable Phase Change Material Emulsions for Thermal Energy Storage and Thermal Management Applications: A Review

A comprehensive review of critical analysis of biodegradable waste PCM for thermal energy storage systems using machine learning and deep learning to predict dynamic behavior

This article explores the use of phase change materials (PCMs) derived from waste, in energy storage systems. It emphasizes the potential of these PCMs in addressing concerns related to fossil fuel usage and environmental impact. This article also highlights the aspects of these PCMs including reduced reliance on renewable resources minimized greenhouse gas emissions and waste reduction. The study also discusses approaches such as integrating nanotechnology to enhance thermal conductivity and utilizing machine learning and deep learning techniques for predicting dynamic behavior. The article provides an overall view of research on biodegradable waste-based PCMs and how they can play a promising role in achieving energy-efficient and sustainable thermal storage systems. However, specific conclusions drawn from the presented results are not explicitly outlined, leaving room, for investigation and exploration in this evolving field. Artificial neural network (ANN) predictive models for thermal energy storage devices perform differently. With a 4% adjusted mean absolute error, the Gaussian radial basis function kernel Support Vector Regression (SVR) model captured heat-related charging and discharging issues. The ANN model predicted finned tube heat and heat flux better than the numerical model. SVM models outperformed ANN and ANFIS in some datasets. Material property predictions favored gradient boosting, but Linear Regression and SVR models performed better, emphasizing application- and dataset-specific model selection. These predictive models provide insights into the complex thermal performance of building structures, aiding in the design and operation of energy-efficient systems. Biodegradable waste-based PCMs' sustainability includes carbon footprint, waste reduction, biodegradability, and circular economy alignment. Nanotechnology, machine learning, and deep learning improve thermal conductivity and prediction. Circular economy principles include waste reduction and carbon footprint reduction. Specific results-based conclusions are not stated. Presenting a comprehensive overview of current research highlights biodegradable waste-based PCMs' potential for energy-efficient and sustainable thermal storage systems.

Investigating the Phase Transition Kinetics of 1-Octadecanol/Sorbitol Derivative/Expanded Graphite Composite Phase Change Material with Isoconversional and Multivariate Non-Linear Regression Methods

Organic composite phase change materials (PCMs) have been extensively studied, and it is important to investigate the effect of added components on the phase change process of the organic matrix. Herein, the phase transition process of the composite PCM with 1-octadecanol (OD) as the matrix adsorbed by a network framework composed of 1,3:2,4-di-(3,4-dimethyl) benzylidene sorbitol (DMDBS) and expanded graphite (EG) was measured using differential scanning calorimetry (DSC) at several linear heating rates. Using isoconversional and multivariate non-linear regression methods, a two-step consecutive reaction model for the composite PCM was established, while the apparent activation energies and pre-exponential factors were determined. The reaction mechanism of the first step was altered compared to pure OD, while the activation energies significantly decreased at the initial stage of the phase transition process and increased at the later stage. Combined with microscopic morphology analysis, the main reasons were the size and nanoconfinement effect. The predictions of the composite PCM under various conditions suggested that the composite PCM had a wider available temperature range compared to pure OD. This research provided a new idea for the in-depth study of the phase transition process of organic composite PCMs, which was helpful for the evaluation of organic composite PCMs.

Miscanthus-Derived Energy Storage System Material Production

Carbon derived from various biomass sources has been evaluated as support material for thermal energy storage systems. However, process optimization of Miscanthus-derived carbon to be used for encapsulating phase change materials has not been reported to date. In this study, process optimization to evaluate the effects of selected operation parameters of pyrolysis time, temperature, and biomass:catalyst mass ratio on the surface area and pore volume of produced carbon is conducted using response surface methodology. In the process, ZnCl₂ is used as a catalyst to promote high pore volume and area formation. Two sets of optimum conditions with different pyrolysis operation parameters in order to produce carbons with the highest pore area and volume are determined as 614 °C, 53 min, and 1:2 biomass to catalyst ratio and 722 °C, 77 min, and 1:4 biomass to catalyst ratio with 1415.4 m²/g and 0.748 cm³/g and 1499.8 m²/g and 1.443 cm³/g total pore volume, respectively. Carbon material produced at 614 °C exhibits mostly micro- and mesosized pores, while carbon obtained at 722 °C comprises mostly of meso- and macroporous structures. Findings of this study demonstrate the significance of process optimization for designing porous carbon material to be used in thermal and electrochemical energy storage systems.