COVENANT JOURNAL OF ENGINEERING TECHNOLOGY

A Review of the Energy and Exergy Analysis of a Cascade Refrigeration System for Process Optimization

2024-02-18T07:49:19+00:00

An overview of thermodynamic energy and exergy analysis is provided in this work with the goal of developing, constructing, and improving a cascade cooling apparatus for extremely low-temperature managing goods and gas-efficient building operations. A review of the classes of cascade systems was carried out, stating the benefits and setbacks. Every part of the refrigeration mechanism cascades was identified, explained, and designed theoretically. Energy and exergy techniques were used to analyze every component of the system separately, model it, and evaluate it to minimize energy destruction. Necessary equations for calculating energy and exergy destructions were outlined to ensure effective modeling and optimization of the system processes. Process losses were identified, determined, and reduced using the application of exergy analysis. There was an improvement in comprehension of the process's deceptive efficiency and energetic effectiveness. The result aids in detecting the locations of energy degradation and mapping out the system's optimal performance. By lowering operational and process design costs and resolving energy- associated environmental issues, the analysis eventually contributed to sustainable growth. This offers a rationale for enhancing an exceptionally low-temperature freezer's functionality for the handling of susceptible-to-heat vaccines. The effective modeling and building of cascade refrigeration systems for zero energy destruction and high efficiency are made possible.

An Experimental Investigation into the Effects of Using Partially Substituted Cassava Peel Ash for Cement in Concrete

2024-06-14T12:17:59+00:00

When placed in landfills, agricultural wastes have been a significant source of contamination to the environment. The rate of consumption of cement being an essential part of concrete cannot be overemphasized. There is need to explore alternative supplementary binding material which is eco-friendly and sustainable towards the production of green concrete. The purpose of this study is to explore the possibility of using cassava peel ash (CPA) as a partial cement substitute in concrete. The partial replacement was achieved in differing percentages of 0%, 1%, 2%, 3%, 4% and 5% by weight of cement in the M20 concrete mix, making use of mix ratio 1:1.5:3. The batched concrete mix samples were cast in cube and cylinder moulds of 100 x 100 x 100mm and 100 by 200 mm respectively and cured for 7, 14, 28, 56 and 90 days. On the fresh concrete mixtures, slump tests were carried out and the split tensile and compressive strengths of the cured concrete cylinders and cubes were evaluated respectively. In the findings derived from the slump test, it is evident that with the incremental augmentation of the percentage replacement of (CPA) within the concrete mixture, there is a discernible augmentation in the workability of the resultant mixture. The results indicated that at 1% cassava peel ash (CPA) replacement, the optimal compressive strength and split tensile strength values were 32.9 N/mm² and 3.9 N/mm², respectively. These values are comparable to those of the control mix with compressive and tensile strength values of 33.1 N/mm² and 4.1 N/mm², respectively. This research investigation unveils the potential suitability of (CPA) as a prospective partial substitute for cement within the composition of a concrete mixture.

A Performance Comparison of Three Machine Learning Algorithms for Urban Land Cover Classification using High-Resolution Imagery

2024-06-10T12:28:14+00:00

Urban land cover classification using high-resolution imagery is important for many applications where detailed and precise urban land cover products are needed. Machine learning algorithms are currently some of the most commonly used methods for classifying high-resolution imagery due to their impressive capabilities. However, the reliability of the land cover products obtained from the classification of high-resolution urban imageries is dependent upon the accuracy of the Machine Learning (ML) classification algorithm used. The need for an appropriate selection of classifiers for urban land cover classification and their applicable settings necessitates the performance comparison of major ML algorithms used for classification. In this study, we compared the performance of three major Machine Learning (ML) classifier algorithms using a high-resolution image dataset of an urban area. The algorithms are Support Vector Machine (SVM), Naïve Bayes, and Ensemble classifiers. The performance of three model types of SVM classifiers namely Medium Gaussian, Linear, and Quadratic SVM, two model types of Naïve Bayes classifiers namely Gaussian and Kernel Naïve Bayes, and three model types of ensemble classifiers namely Bagged Trees, Subspace Discriminant, and RUSBoosted Trees were compared. Performance evaluation was carried out using Confusion Matrix (CM) and Receiver Operating Curves (ROC) plots. Results obtained from the comparison of the three ML classifier algorithms show that the Subspace Discriminant ensemble classifier had the highest accuracy at 85.1%, closely followed by the Medium Gaussian SVM classifier (84.5%) and Gaussian Naïve Bayes classifier (81.5%). This research provides insights into the selection of classifiers for future urban land cover classification and their applicable settings.