dr hab. inż. Marek Dzida | Gdańsk University of Technology

The rapid depletion of fossil-derived fuels along with rising environmental pollution have motivated academics and manufacturers to pursue more environmentally friendly and sustainable energy options in today’s globe. Biodiesel has developed as an ecologically favorable alternative. However, the mass manufacturing of biodiesel on an industrial scale confronts substantial cost and pricing challenges. To address this issue, high-efficiency...

In this study, eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting (LightGBM) algorithms were used to model-predict the drying characteristics of banana slices with an indirect solar drier. The relationships between independent variables (temperature, moisture, product type, water flow rate, and mass of product) and dependent variables (energy consumption and size reduction) were established. For energy consumption,...

This study assessed the usefulness of algorithms in estimating energy consumption and carbon dioxide emissions in Viet- nam, in which the training dataset was used to train the models linear regression, random forest, XGBoost, and AdaBoost, allowing them to comprehend the patterns and relationships between population, GDP, and carbon dioxide emissions, energy consumption. The results revealed that random forest, XGBoost, and AdaBoost...

Thermoacoustic technologies are considered an effective solution for harnessing low-temperature heat, whether from waste or renewable sources. However, in practice, developing and implementing high-performance ther- moacoustic systems is a complex challenge. In real waste heat recovery systems, heat exchange between ther- moacoustic engines (TAEs) and external heat sources is facilitated by auxiliary systems, such as circulation...

Examining the game-changing possibilities of explainable machine learning techniques, this study explores the fast-growing area of biochar production prediction. The paper demonstrates how recent advances in sensitivity analysis methodology, optimization of training hyperparameters, and state-of-the-art ensemble techniques have greatly simplified and enhanced the forecasting of biochar output and composition from various biomass...