Advanced Approaches Applied to Materials Development and Design Predictions

This thematic issue on advanced simulation tools applied to materials development and design predictions gathers selected extended papers related to power generation systems, presented at the XIX International Colloquium on Mechanical Fatigue of Metals (ICMFM XIX), organized at University of Porto, Portugal, in 2018. In this issue, the limits of the current generation of materials are explored, which are continuously being reached according to the frontier of hostile environments, whether in the aerospace, nuclear, or petrochemistry industry, or in the design of gas turbines where efficiency of energy production and transformation demands increased temperatures and pressures. Thus, advanced methods and applications for theoretical, numerical, and experimental contributions that address these issues on failure mechanism modeling and simulation of materials are covered. As the Guest Editors, we would like to thank all the authors who submitted papers to this Special Issue. All the papers published were peer-reviewed by experts in the field whose comments helped to improve the quality of the edition. We also would like to thank the Editorial Board of Materials for their assistance in managing this Special Issue

Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting

More accurate and precise energy demand forecasts are required when energy decisions are made in a competitive environment. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated. Examples include seasonality, cyclicity, fluctuation, dynamic nonlinearity, and so on. These forecasting models have resulted in an over-reliance on the use of informal judgment and higher expenses when lacking the ability to determine data characteristics and patterns. The hybridization of optimization methods and superior evolutionary algorithms can provide important improvements via good parameter determinations in the optimization process, which is of great assistance to actions taken by energy decision-makers. This book aimed to attract researchers with an interest in the research areas described above. Specifically, it sought contributions to the development of any hybrid optimization methods (e.g., quadratic programming techniques, chaotic mapping, fuzzy inference theory, quantum computing, etc.) with advanced algorithms (e.g., genetic algorithms, ant colony optimization, particle swarm optimization algorithm, etc.) that have superior capabilities over the traditional optimization approaches to overcome some embedded drawbacks, and the application of these advanced hybrid approaches to significantly improve forecasting accuracy

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Experimental investigation and modelling of the heating value and elemental composition of biomass through artificial intelligence

Abstract: Knowledge advancement in artificial intelligence and blockchain technologies provides new potential predictive reliability for biomass energy value chain. However, for the prediction approach against experimental methodology, the prediction accuracy is expected to be high in order to develop a high fidelity and robust software which can serve as a tool in the decision making process. The global standards related to classification methods and energetic properties of biomass are still evolving given different observation and results which have been reported in the literature. Apart from these, there is a need for a holistic understanding of the effect of particle sizes and geospatial factors on the physicochemical properties of biomass to increase the uptake of bioenergy. Therefore, this research carried out an experimental investigation of some selected bioresources and also develops high-fidelity models built on artificial intelligence capability to accurately classify the biomass feedstocks, predict the main elemental composition (Carbon, Hydrogen, and Oxygen) on dry basis and the Heating value in (MJ/kg) of biomass...Ph.D. (Mechanical Engineering Science

水熱法による農業廃棄物からの付加価値製品の研究

The oil palm industry\u27s steady growth over the past decade has resulted in the increasing production of waste solid and liquid biomass such as oil palm frond (OPF) and palm oil mill effluent (POME). Current disposal practices are not environmentally friendly and undermine the vast potential of biomass as a renewable resource. Due to this problem, research regarding the utilization and valorization of oil palm biomass as raw material for the production of value-added products has gained interest. Many new methods and processes to treat biomass and produce materials such as cellulose-based materials, biochar, activated carbon, and biofuels have been reported, but adoption by the industry has been lackluster. There has been a figurative wall that inhibits implementation and adoption, far beyond bench and pilot scale. Among the factors that inhibit adoption include complicated processes that require specialized equipment and chemicals, multiple pretreatment steps that increase the time required, and waste that needs to be disposed of, besides related increased cost to the industry. The research community must develop simpler, appropriate technology in utilizing oil palm biomass that can be easily adopted by the industry. This study presents several methods to try to solve the problems stated above. By utilizing the hydrothermal process, biomass (oil palm frond) and its derivative (oil palm empty fruit bunch biochar) were treated using nitric acid as oxidizing agent to remove lignin and in the case of biochar, to improve its performance via additional surface functional groups. For the hydrothermal lignin removal process, treated oil palm frond (OPF) lignin content decreased by 86.5% after 30 min of treatment at 120°C. Cellulose yield was 68.2% which is comparable to other previously reported literature. Further analysis using TGA, FTIR, and XRD concluded that hydrothermally treated OPF has similar thermal stability, surface chemical property, and crystallinity to commercially available cellulose products (microcrystalline cellulose). The process was also applied to oil palm empty fruit bunch (OPEFB) and Matake bamboo without any modification and pretreatment. Both biomass shows similar lignin reduction and properties such as treated OPF which showed that the process can be applied to other types of biomass without significant parameter modification. Hydrothermally treated biomass still showed a significant amount of hemicellulose which can be problematic especially in the production of cellulose-based composites and fibers. Removal of hemicellulose via superheated steam (SHS) treatment is proposed due to the excellent hemicellulose removal efficiency reported in the literature. It was found that the process produced biomass that can dissolve in sodium hydroxide (NaOH) solution at room temperature. This property was novel since the dissolution of cellulose in alkali solution usually requires additives such as urea and thiourea, and sub-zero temperature. NaOH-soluble biomass was analyzed via TGA, FTIR, and XRD which showed similar physical and chemical characteristics as normal cellulose fiber (MCC). 13C cross-polarization magic angle spinning (CPMAS) nuclear magnetic resonance (NMR) spectroscopy analysis suggested that the reduction in intra-chain and inter-sheet hydrogen bonding strength is the contributing factor in the increased solubility of cellulose after SHS treatment. Besides direct biomass treatment, the hydrothermal process was also applied to biochar from oil palm biomass (OPEFB) to improve its performance in adsorbing dye and heavy metals. The process successfully increased the amount of surface functional groups without significant change in the surface morphology based on SEM, BET surface area, FTIR, and EDX analysis. Adsorption isotherm experiments suggested that the adsorption process occurred following the Langmuir isotherm model and pseudo-second-order kinetic model. Intra-particle diffusion model analysis suggested multiple stages of adsorption. Adsorption performance of functionalized biochar in removing dye and heavy metal from aqueous solution showed improved performance, with almost 7x increase in methylene blue adsorption capacity, and up to 6x increase in removal percentage for heavy metal adsorption. This result suggested that biochar performance can be improved through functionalization, and the process can be done using the hydrothermal method. The results presented in this thesis provide new methods and processes that can be easily implemented in the industry due to its simplicity, lower usage of chemicals, and utilizing available resources in the palm oil mill industry such as hydrothermal treatment (fresh fruit bunch sterilizer) and steam from the boiler. This result can help promotes better technology for the oil palm industry.九州工業大学博士学位論文 学位記番号：生工博甲第416号 学位授与年月日：令和3年9月24日1 Introduction|2 Literature Review|3 Hydrothermal Lignin Removal from Oil Palm Biomass|4 Combined Hydrothermal and Superheated Steam (SHS) Treatment on Biomass|5 Hydrothermal Surface Functionalization of Oil Palm Biochar and Other Carbon Material|6 Conclusion and Recommendations九州工業大学令和3年

Optimisation, Optimal Control and Nonlinear Dynamics in Electrical Power, Energy Storage and Renewable Energy Systems

The electrical power system is undergoing a revolution enabled by advances in telecommunications, computer hardware and software, measurement, metering systems, IoT, and power electronics. Furthermore, the increasing integration of intermittent renewable energy sources, energy storage devices, and electric vehicles and the drive for energy efficiency have pushed power systems to modernise and adopt new technologies. The resulting smart grid is characterised, in part, by a bi-directional flow of energy and information. The evolution of the power grid, as well as its interconnection with energy storage systems and renewable energy sources, has created new opportunities for optimising not only their techno-economic aspects at the planning stages but also their control and operation. However, new challenges emerge in the optimization of these systems due to their complexity and nonlinear dynamic behaviour as well as the uncertainties involved.This volume is a selection of 20 papers carefully made by the editors from the MDPI topic “Optimisation, Optimal Control and Nonlinear Dynamics in Electrical Power, Energy Storage and Renewable Energy Systems”, which was closed in April 2022. The selected papers address the above challenges and exemplify the significant benefits that optimisation and nonlinear control techniques can bring to modern power and energy systems

Plant Extracts

Society has recently demonstrated a high level of awareness and responsibility concerning environmental issues. The interest in bioactive compounds extracted from natural sources has increased due to their potential application as active ingredients in several industries, particularly the cosmetic, food, and pharmaceutical industries. Plants are rich sources of phenolic compounds that have been widely studied due to their health-promoting properties, namely antioxidant, anti-carcinogenic, and anti-inflammatory activities, among others. Extraction is usually the limiting analytical step in the yield of bioactive compounds. From a green point of view, many extraction techniques have been employed as potential candidates to replace conventional methods, such as ultrasound-assisted extraction (UAE), pressurized liquid extraction (PLE), microwave-assisted extraction (MAE), supercritical fluid extraction (SFE), pulsed electric field extraction, and enzyme-assisted extraction. In this Special Issue, we focus our attention on the chemical characterization of plant extracts and their bioactive composition, focusing also on in-vitro cell assays and molecular tools. The issue comprises original research articles, as well as a review, on topics such as phenolic profile, radical scavenging capacity, in vitro cell assays, comet assay, and antimicrobial capacity. We close this Special Issue with a review paper that focuses on the pharmacological activities of quercetin, one of the principal polyphenols. With this, we aim to provide a contemporary overview of the advantages of bioactive compounds extracted from plants

A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics

The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at an accelerated pace. Because of that, in spite of the fact that there are a lot of surveys and reviews in the field, they quickly become dated. Thus, it is of importance to keep pace with the current developments. In this review, we first consider a possible classification of bio-inspired multiparameter optimization methods because papers dedicated to that area are relatively scarce and often contradictory. We proceed by describing in some detail some more prominent approaches, as well as those most recently published. Finally, we consider the use of biomimetic algorithms in two related wide fields, namely microelectronics (including circuit design optimization) and nanophotonics (including inverse design of structures such as photonic crystals, nanoplasmonic configurations and metamaterials). We attempted to keep this broad survey self-contained so it can be of use not only to scholars in the related fields, but also to all those interested in the latest developments in this attractive area

The Application of Nature-inspired Metaheuristic Methods for Optimising Renewable Energy Problems and the Design of Water Distribution Networks

This work explores the technical challenges that emerge when applying bio-inspired optimisation methods to real-world engineering problems. A number of new heuristic algorithms were proposed and tested to deal with these challenges. The work is divided into three main dimensions: i) One of the most significant industrial optimisation problems is optimising renewable energy systems. Ocean wave energy is a promising technology for helping to meet future growth in global energy demand. However, the current technologies of wave energy converters (WECs) are not fully developed because of technical engineering and design challenges. This work proposes new hybrid heuristics consisting of cooperative coevolutionary frameworks and neuro-surrogate optimisation methods for optimising WECs problem in three domains, including position, control parameters, and geometric parameters. Our problem-specific algorithms perform better than existing approaches in terms of higher quality results and the speed of convergence. ii) The second part applies search methods to the optimization of energy output in wind farms. Wind energy has key advantages in terms of technological maturity, cost, and life-cycle greenhouse gas emissions. However, designing an accurate local wind speed and power prediction is challenging. We propose two models for wind speed and power forecasting for two wind farms located in Sweden and the Baltic Sea by a combination of recurrent neural networks and evolutionary search algorithms. The proposed models are superior to other applied machine learning methods. iii) Finally, we investigate the design of water distribution systems (WDS) as another challenging real-world optimisation problem. WDS optimisation is demanding because it has a high-dimensional discrete search space and complex constraints. A hybrid evolutionary algorithm is suggested for minimising the cost of various water distribution networks and for speeding up the convergence rate of search.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 202

Renewable Energy Resource Assessment and Forecasting

In recent years, several projects and studies have been launched towards the development and use of new methodologies, in order to assess, monitor, and support clean forms of energy. Accurate estimation of the available energy potential is of primary importance, but is not always easy to achieve. The present Special Issue on ‘Renewable Energy Resource Assessment and Forecasting’ aims to provide a holistic approach to the above issues, by presenting multidisciplinary methodologies and tools that are able to support research projects and meet today’s technical, socio-economic, and decision-making needs. In particular, research papers, reviews, and case studies on the following subjects are presented: wind, wave and solar energy; biofuels; resource assessment of combined renewable energy forms; numerical models for renewable energy forecasting; integrated forecasted systems; energy for buildings; sustainable development; resource analysis tools and statistical models; extreme value analysis and forecasting for renewable energy resources