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The optimization of 3D-printed drone performance remains a significant challenge in achieving both efficiency and cost-effectiveness. Traditional single-algorithm approaches often fail to address the complex interplay between structural dynamics and energy efficiency requirements. Current approaches lack integrated optimization techniques that could complement each other's strengths in addressing these multifaceted challenges in drone design and performance. This paper presents a novel multi-algorithm optimization framework integrating Response Surface Methodology with Box-Behnken Design (RSM-BBD), Artificial Neural Networks (ANNs), and Osprey Optimization Algorithm (OOA). The approach systematically optimizes three critical parameters: frame length (20-30 cm), DC brushless motor (1250-1750 kV), and flight duration (3-5 minutes). Finite Element Analysis (FEA) validates the structural integrity of the optimized components under operational stresses. The integrated framework demonstrates complementary strengths across methods. RSM-BBD achieved superior results in battery consumption optimization with R² = 0.9801, while OOA excelled in stability enhancement by determining optimal parameters (20 cm frame length, 1456.47 kV brushless motor, and 3.69 minutes flight duration). ANNs effectively captured overall system dynamics with R² = 0.97362, providing comprehensive performance prediction. This synergistic approach establishes a new paradigm for drone optimization, maintaining manufacturing costs at $115.79 while delivering significant performance improvements. The framework provides a foundation for future developments in cost-effective, high-performance drone manufacturing and design optimization.

Electrospinning has emerged as a versatile technique for fabricating polymer nanofibers with diameters ranging from tens to hundreds of nanometers. Precisely controlling and predicting the nanofiber diameter is crucial for various applications. The complex interplay of multiple electrospinning parameters presents a significant challenge in this endeavor. This study introduces a novel data-driven approach using machine learning (ML) to predict and optimize the diameter of electrospun nanofibers. A comprehensive dataset of approximately 430 data points was compiled from literature sources, including polymer properties and electrospinning process parameters. Six ML algorithms were evaluated: Decision Tree (DT), Extra Trees Regression (ETR), Kernel Ridge Regression (KRR), Random Forest (RF), Support Vector Regression (SVR), and eXtreme Gradient Boosting (XGB). A 75/25 train-test split was selected for optimal model evaluation, with 10-fold cross-validation employed for hyperparameter tuning and estimating the variability of performance metrics. RF and ETR demonstrated the best predictive accuracy, with R2 values of 0.9468 and 0.9421, and RMSE values of 92.3 nm and 96.1 nm, respectively, on the test set. Feature importance analysis and SHapley Additive exPlanations (SHAP) values revealed that polymer concentration, applied voltage, and feed rate significantly influence the nanofiber diameter, which was further elucidated by partial dependence plots. To facilitate accessibility and collaboration, an interactive web server called ENDP was developed, allowing users to input polymer properties and process parameters to predict the nanofiber diameter. This study showcases the potential of ML in guiding the design and optimization of electrospun nanofibers, providing valuable insights for tailoring their properties into specific applications.

In recent years, lattice structures have gained extensive attention due to their unique properties and great potential in various fields. Their effective material properties can be obtained by several means, e.g., numerical homogenization methods, finite element (FE) simulations, and experiments. Numerical homogenization methods involve certain assumptions, and thus, their applications are limited to those lattice structures satisfying all assumptions. On the contrary, although the FE simulations and experiments can be performed for all lattice structures, they are more time-consuming and tedious than the numerical homogenization methods, especially for lattice structures with complex geometry. In this study, a surrogate model is presented to address all the drawbacks mentioned above. More precisely, the effective elastic modulus of two-dimensional lattice structures with triangular unit cells is predicted by a surrogate model trained with the random forest algorithm. The dataset is generated from FE simulations. Several geometric details along with the elastic modulus of the parent material are selected as input features. These input features are varied to cover a wide range of lattice structures encountered in engineering applications. The correlation between input features is investigated to test their independence. Furthermore, the contribution of each input feature to the prediction of the surrogate models is also thoroughly examined. The accuracy of the proposed surrogate model is evaluated through comparisons with FE simulations, numerical homogenization methods, and experiments for untrained datasets.

This study explores the current technology landscape and intersection of biochar and carbon capture and storage (CCS) within the engineering field, presenting a meticulous analysis gleaned from the Scopus database through bibliometric analysis. In response to the urgent need to address the escalating climate crisis, biochar, with its high carbon content, emerges as a promising and resilient tool for carbon sequestration. A literature review establishes biochar's pivotal role in mitigating climate change with contributions including substantial carbon sequestration potential, economic benefits, and positive impacts on soil structure and crop yields. Distinguishing between the applications of biochar and CCS, this paper emphasizes their complementary roles in decarbonization. By employing VOSviewer, an advanced bibliometric tool, a quantitative exploration of global connections identifying prominent authors, highly cited literature, and research trends is provided. The results reveal a substantial increase in publications related to biochar in CCS, particularly during the rapid development phase from 2016 to 2023, reflecting a growing interest in utilizing biochar as a carbon sink. Key insights from the co-occurrence analysis of keywords shed light on evolving research focuses, with three distinct clusters demonstrating the interconnectedness of adsorption, biochar, and pyrolysis. The precise method highlights a shift in research focus towards more impactful areas, particularly water pollutant removal and adsorption. The conclusion emphasizes biochar's dual role in soil carbon sequestration and carbon capture technologies, showcasing its versatility as a valuable tool in climate change mitigation efforts. Despite challenges in large-scale implementation, biochar, especially in the context of direct air capture and bioenergy CCS, emerges as a cost-effective and environmentally friendly adsorbent. In summary, this bibliometric analysis encapsulates a rigorous exploration of biochar and CCS, contributing valuable insights for researchers, policymakers and practitioners. By navigating uncharted territory, this study guides future endeavors toward impactful and relevant areas of study in the pursuit of sustainable climate change mitigation.

Abstract Sustainable urban development is an increasingly important concept as cities around the world continue to grow and face challenges related to urbanization, including environmental degradation, social inequality, and economic instability. Chiang Mai is a rapidly growing city in Thailand that steers towards sustainability. In this work, we examine the state of sustainable urban development in Chiang Mai by analyzing various indicators, including transportation, waste management, air quality, and energy consumption. A multi-dimensional framework was used to assess the sustainability of Chiang Mai. Our analysis suggests that while Chiang Mai has made progress in some areas of sustainable urban development, such as promoting green transportation and waste reduction and reduction consumption, there are still many challenges to be addressed, particularly in air pollution, water management, transportation and energy consumption. We conclude by discussing the implications of our findings for policy makers, urban planners, and other stakeholders interested in promoting sustainable urban development. This study assesses Chiang Mai’s sustainability using a comprehensive set of 35 indicators. It was found that Chiang Mai has potential for sustainable development, but some indicators require improvement. The study presents guidelines for prioritizing sustainable development, improving air and water quality, promoting sustainable tourism, enhancing waste management, transportation management and energy consumption. This research provides alternative framework for evaluating cities and presents a valuable contribution to the field of sustainable urban development.

The transportation industry is undergoing a major shift towards electrification to mitigate carbon emissions and decrease reliance on fossil fuels in response to global climate change and greenhouse gas concerns. Recently, battery electric vehicles (BEVs) have made significant progress and are becoming a more viable option for achieving zero-emission transportation. The aim of this study is to investigate the energy consumption patterns of BEVs operating in real-world driving scenarios encompassing various route conditions. The vehicle sensor data employed in this study was acquired through onboard diagnostic devices that directly gathered raw data and subsequently transmitted it to mobile applications. Various significant factors, such as payload, road slope level, speed range, acceleration, and loads of heating, ventilation, and air conditioning, are considered as variables influencing energy consumption. By utilizing the large amount of data collected, machine learning (ML) techniques were applied to develop a predictive model of energy consumption and identify variables that influence energy consumption. The outcomes of this study hold the potential to offer guidance to transportation policymakers and furnish valuable insights for prospective buyers considering BEVs. Furthermore, the application of ML in the development of a predictive model demonstrated efficacy and exhibits promising potential for wider-ranging applications.

Biomass can be converted to bio-fuels and bio-chemicals via many thermo-chemical conversion platforms such as hydrothermal liquefaction (HTL) which occurs at high temperature and pressure conditions. HTL can be used for converting wet or high moisture content biomass to biocrude oils effectively. There are wide variety of relevant parameters affecting the system of biomass HTL, for example, operating conditions, biomass characteristics, solvent properties, or catalyst identities used. This brings about the difficulty in making highly accurate prediction of biocrude oil yields from biomass HTL. In this work, a gradient tree boosting machine learning (GTB-ML) model with principal component analysis (PCA) was applied to predict the yields of biocrude oils from biomass HTL, using 15 input variables from biomass characteristics, operating conditions, and solvent properties. PCA is a statistical method to find correlations in multivariate data, and it can be used to specify highly influential variables in input datasets for ML models. PCA results confirmed that input variables from biomass characteristics were as important as those from HTL operating conditions. The GTB-ML developed from principal components 1 to 8, representing about 90% of the whole original dataset can be used to predict the yields of bio-crude oils from biomass HTL at acceptable accuracy with correlation coefficient, R2=0.8and root mean square error = 0.005.

Electric vehicles (EVs) are alternatives to traditional combustion engine-powered vehicles. This work focuses on a thermal management system for battery EVs using liquid cooling and a machine learning (ML) model to predict their thermal-related health. Real-world data of EV operation, battery and cooling conditions were collected. Key influencing factors on the thermal-related health of batteries were identified. The ML model’s effectiveness was evaluated against experimental test data. The ML model proved effective in predicting and analyzing battery thermal health, suggesting its potential for use with the thermal management system.

Cities around the world have expanded and consumed many resources. The expansion of these cities has had a huge impact on our planet. There are many ideas about sustainable urban development around the world to slow down or stop the destruction of the environment. Sustainable urban development is a concept that allows the economy, society, and environment to grow together in a balanced and sustainable manner. Chiang Mai in Thailand is considered a city with much potential. It currently has good city planning. However, indicators to evaluate the potential of sustainable cities are still lacking. Hence, this work aims to develop appropriate indicators for assessing the sustainability of Chiang Mai city using the Delphi method of panel surveys. At least 20 experts in various fields were selected to take the Delphi surveys conducted in three rounds. The results of the Delphi processes showed that there were 35 indicators suitable for assessing Chiang Mai's potential as a sustainable city and helping with the development planning of Chiang Mai in the future.

Among renewable and sustainable energy resources, biomass plays a vital role. Agricultural residues/wastes, energy crops, and lignocellulosic biomass could potentially be major feedstocks for biorefineries. In Thailand, one of the most interesting energy crops is hybrid giant Juncao grass (GJG) or Pennisetum purpureum × Pennisetum typhoideum. GJG can be easily grown and has relatively high yields under tropical climates. Herein, conversion of GJG to biofuels via hydrothermal liquefaction (HTL) was investigated using batch reactors under varying reaction temperatures of 250–350 °C and biomass-to-deionized water concentrations of 15–25 wt% at a fixed residence time of 30 min. Changes in temperature and GJG-to-deionized water concentration were found to markedly affect the yields and distribution of products from HTL of GJG. Yields of the liquid product, or bio-oil, can be up to 50 wt% at 350 °C and 25 wt% GJG-to-deionized water concentration. The yields of solid char and gas products fluctuated within 10–25 wt% and 30–45 wt%, respectively. Higher heating values of the resulting bio-oil and char were remarkably better than those of the raw material. An energy recovery of over 50% from the bio-oil, as well as about 35% from the char, can be obtained. By gas chromatograph-mass spectrometry and nuclear magnetic resonance, the bio-oil obtained was found to be a complex chemical mixture, consisting mainly of phenols, nitrogenous compounds, aliphatic compounds, ketones, carboxylic acids, and aldehydes. The finding is useful in future utilization of GJG via HTL for biofuel and/or biochemical production.

Transport electrification, which entails replacing fossil fuel-powered engines with electric drivetrains through the use of electric vehicles (EVs), has been identified as a potential strategy for reducing emissions in the transportation sector. As the adoption of EVs increases, there is a growing need to understand their performance and characteristics, particularly the factors that influence energy consumption under actual driving conditions. This study sought to investigate the actual energy consumption of commercial battery electric vehicles (BEVs) in Thailand by conducting real-world driving tests under various route conditions, including urban and rural route modes. Data collection was performed through the use of onboard diagnostics and global positioning system devices. The result shows that the average energy consumption of the BEVs in this study was 148.03 Wh/km. Moreover, several machine learning (ML) techniques were utilized to analyze the collected dataset to predict energy consumption and identify the key factors influencing energy consumption. A comprehensive investigation of factor significance was carried out by employing a specific algorithm in conjunction with the SHapley Additive exPlanations (SHAP) approach. This investigation provided insights into the influence of battery current and vehicle speed on the energy consumption of BEVs, particularly in the context of urban route conditions. The results of this study provide valuable insights into the energy consumption of BEVs and the factors affecting it, which can aid in improving energy efficiency and informing policy decisions related to transport electrification.

Background Energy shortages and global warming have been significant issues throughout history. Therefore, the search for environmentally friendly renewable energy sources is crucial for achieving sustainability. Biomass energy is gaining global attention as a renewable energy option, particularly through the process of hydrothermal liquefaction, which converts wet biomass into bio-crude oil. Methods Hydrothermal liquefaction is a complex process that is challenging to explain, leading to research on machine learning models for this process. These models aim to predict values and investigate the impact of variables on the hydrothermal liquefaction process. These models aim to predict values and investigate the impact of variables on the hydrothermal liquefaction process. However, the development of machine learning in hydrothermal liquefaction is still limited due to its novelty and the time required for comprehensive study. Thus, the objective of this study was to analyze relevant publications in the Scopus database, focusing on indexed ML and HTL keywords, to understand keyword associations and co-citations. Results The results reveal an increasing trend in the study of ML in the HTL process, with a growing interest from various countries. Conclusion Notably, China currently holds the largest share of ML research in HTL processes, with most published works falling within the field of engineering. The keyword “liquefaction” emerges as the most popular term in these publications.