Electromagnetic heating processes: analysis and simulations

Electromagnetic heating (EMH) processes are being increasingly used in the industrial and domestic sectors, yet they receive relatively little attention in the thermal engineering domain. Time-temperature characteristics in EMH are qualitatively different from those in conventional heating techniques due to the additional parameters (viz dielectric properties of the material, size and shape of the product and process frequency). From a unified theory perspective, a multi-purpose model has been developed in order to obtain the heating characteristics for an arbitrary processing situation. Theoretical analyses of various EMH processes in materials of various regular geometries and a range of physical properties have been undertaken. Despite the wide spread usage of microwave energy in the food engineering sector. few understand microwaves and their interactions with foods. Much of the published research is largely focussed from the view point of an electrical engineer and aimed at the oven designer. However, trial-and-error methods are usually employed when developing microwavable food products and when using microwave ovens. The presented thesis is focussed from the view-point of the thermal engineer and aimed primarily at food developers and end users. The multi-purpose model was then modified specifically for simulating the heating of food materials in a microwave oven. The validity of the commonly made assumptions was investigated; in particular the variation of dielectriC properties during the heating processes and their likely influence on the model's predictions. Experimental data available in the literature were compiled and analysed to form a set of equations for predicting the dielectric properties of various food materials. Also available correlations for thermal properties were evaluated for a selected set of experimental data of different food materials. Analyses were undertaken to demonstrate and evaluate the effects of various parameters on the heating characteristics of different food materials commonly heated/cooked in microwave ovens. A qualitative comparison of model predictions and experimental measurements is provided to validate the physical basis of the model. Findings from the model lead to a better understanding of the interactions between foods and microwaves. [...cont.

Role and Important Properties of a Membrane with Its Recent Advancement in a Microbial Fuel Cell

Microbial fuel cells (MFC) are an emerging technology for wastewater treatment that utilizes the metabolism of microorganisms to generate electricity from the organic matter present in water directly. The principle of MFC is the same as hydrogen fuel cell and has three main components (i.e., anode, cathode, and proton exchange membrane). The membrane separates the anode and cathode chambers and keeps the anaerobic and aerobic conditions in the two chambers, respectively. This review paper describes the state-of-the-art membrane materials particularly suited for MFC and discusses the recent development to obtain robust, sustainable, and cost-effective membranes. Nafion 117, Flemion, and Hyflon are the typical commercially available membranes used in MFC. Use of nonfluorinated polymeric membrane materials such as sulfonated silicon dioxide (S-SiO2) in sulfonated polystyrene ethylene butylene polystyrene (SSEBS), sulfonated polyether ether ketone (SPEEK) and graphene oxide sulfonated polyether ether ketone (GO/SPEEK) membranes showed promising output and proved to be an alternative material to Nafion 117. There are many challenges to selecting a suitable membrane for a scaled-up MFC system so that the technology become technically and economically viable

Simplified Thermal Performance Evaluation of a PCM-Filled Triple-Glazed Window under Arctic Climate Conditions

This paper evaluates the thermal performance of a triple-glazed glass window filled with a phase-change material (PCM) compared to the performance of a traditional triple-glazed window with air gaps. The chosen PCM was paraffin wax. A mathematical model to simulate heat transfer within the system was presented. A commercially available software, COMSOL Multiphysics, was used to numerically solve the governing equations. The analysis was carried out for the representative days of different seasons using three types of paraffin wax (5, 10, and 15) that have different melting-temperature ranges. Particularly, the study considers the unique climatic conditions of the Arctic region. Results showed that by integrating a PCM into the cavity of triple-glazing, thermal performance during summer season of the window was enhanced, while for spring and autumn thermal performance was affected by the type of paraffin selected. The thermal performance of glass windows during winter did not change with PCM integration

Effect of pH, COD, and HRT on the Performance of Microbial Fuel Cell Using Synthetic Dairy Wastewater

Microbial fuel cells (MFC) are emerging technologies that can produce electricity while treating wastewater. A series of tests were carried out to evaluate the efficiency of this technology for treating dairy wastewater (DWW). The experiments used Shewanella baltica as an exoelectrogen in a small single MFC to treat simulated DWW. The impacts of various operational factors, specifically pH, hydraulic retention time (HRT), and chemical oxygen demand (COD) in the influent to the anode chamber, were investigated, and the effect of these variables on the output performance of the cell was evaluated. The best performance of the MFC was found when the pH, HRT, and COD were 8, 6.66 h, and 20,632 mg/L, respectively, in the scaled experimental setup. Under these conditions, the maximum power density and percentage removal of COD in terms of wastewater treatment ability were found to be 138 mW/m2 and 71%, respectively. It may be concluded that MFCs are suitable treatment technologies for treating dairy wastewater while potentially simultaneously generating power

A Techno-economic Study of a Biomass Gasification Plant for the Production of Transport Biofuel for Small Communities

A techno-economic feasibility study of liquid bio-fuel production from biomass to meet the demand for public transport in small communities is presented. The methodology adopted in this work is based on calculating the demand of fuels required by transport sector and then estimating the amount of available biomass from various sources which can be treated to produce biofuels to meet the demand within the region. Depending on demand and available biomass feedstock, size and type of the gasification plant are specified. Narvik, a town in the northern part of Norway, is considered as a case study. The current demand of diesel for public transport in Narvik was calculated. The main sources of biomass in the region under consideration come basically from forests and municipal solid waste. It was found out that the potential of producing biofuel is more than three times the fuel demand for public transport, which means that excess biofuel produced can be used in other sectors such as heating. A downdraft gasifier of 6.0 MW was considered adequate to produce the required amount of biofuel. Cost analysis was performed where capital cost, operational and maintenance (O&M) costs for the biomass pre-treatment processes, the gasification plant and the gas to liquid (GTL) plant were considered in the assessment. It was concluded that the payback period of the project could be achieved within four years. The study demonstrated that biomass gasification offers small communities a means to cover their energy demand for public transport using local biomass feedstock and fulfils environmental targets of the community

A computational intelligence-based maximum power point tracking for photovoltaic power generation system with small‐signal analysis

There are multiple peak functions in its output power characteristic curve of a photovoltaic (PV) array under partial shading conditions (PSCs), the perturb and observe (P&O) may fail to track the global maximum power point (GMPP). Therefore, a reliable maximum power point tracking (MPPT) technique is essential to track the GMPP within an appropriate time. This article proposes a hybrid technique by combining an evolutionary optimization technique, namely the modified invasive weed optimization (MIWO) with the conventional P&O algorithm to enhance the search performance for the maximum power output of the PV system. MIWO executes in the initial stages of the tracking followed by the P&O at the final stages in the MPPT search process. The combined approach ensures faster convergence and better search to the GMPP under rapid climate change and PSCs. The search performance of the hybrid MIWO+P&O technique is examined on a standalone PV system through both MATLAB/Simulink environment and experimentally using dSPACE (DS1103)-based real-time microcontroller hardware setup. The performance of the proposed hybrid MPPT scheme is compared with the recent state-of-the-art MPPPT techniques. In addition, the small-signal analysis of the PV system is carried out to evaluate the loop robustness of the controller design. For a given set of system parameters, simulations for the small-signal model and robustness studies are analyzed to verify the results. The overall results justify the efficacy of the proposed hybrid MPPT algorithm

Artificial Intelligence-Driven Innovations in Hydrogen Safety

This review explores recent advancements in hydrogen gas (H2) safety through the lens of artificial intelligence (AI) techniques. As hydrogen gains prominence as a clean energy source, ensuring its safe handling becomes paramount. The paper critically evaluates the implementation of AI methodologies, including artificial neural networks (ANN), machine learning algorithms, computer vision (CV), and data fusion techniques, in enhancing hydrogen safety measures. By examining the integration of wireless sensor networks and AI for real-time monitoring and leveraging CV for interpreting visual indicators related to hydrogen leakage issues, this review highlights the transformative potential of AI in revolutionizing safety frameworks. Moreover, it addresses key challenges such as the scarcity of standardized datasets, the optimization of AI models for diverse environmental conditions, etc., while also identifying opportunities for further research and development. This review foresees faster response times, reduced false alarms, and overall improved safety for hydrogen-related applications. This paper serves as a valuable resource for researchers, engineers, and practitioners seeking to leverage state-of-the-art AI technologies for enhanced hydrogen safety systems