Universiti Teknologi Malaysia

About: Universiti Teknologi Malaysia is a education organization based out in Johor Bahru, Malaysia. It is known for research contribution in the topics: Membrane & Control theory. The organization has 21644 authors who have published 39500 publications receiving 520635 citations.

An overview of agricultural biomass for decentralized rural energy in Ghana

Abstract: Efforts to improve the quality of life in rural areas rely upon the provision of electrical energy services. Globally, the focus is on identifying and maintaining sustainable and environmentally friendly energy resources, by means of the clean development mechanism (CDM). Supplying electricity by extending the grid to rural domains is, in most cases, economically unproductive, taking into account other related factors that pertain, especially in developing countries. Furthermore, an unfolding energy crisis in the sub-Saharan Africa (SSA) region intensifies the need for decentralized bioenergy applications using modern conversion techniques. Biomass energy produced in rural areas provides a sustainable alternative to grid electricity. This paper presents an overview of the potential of agricultural biomass-based resources for decentralized energy in rural areas of Ghana. It emphasizes the strategic importance of biomass energy, especially in areas where it is economically attractive because of the ready availability of resources. Assimilation of past and current research reported in the literature on biomass resources and bioenergy technologies in the country underpins this study. A more detailed evaluation of agricultural biomass-based potential was carried out and 2010 was chosen as the base period for the assessment. The result suggests that Ghana has a suitable potential of bioenergy resources and this holds considerable promise for future energy delivery in the country. The paper concludes with discussion of various promising decentralized bioenergy technologies for the exploitation of resources in Ghana.

Predicting tunnel boring machine performance through a new model based on the group method of data handling

Abstract: The tunnel boring machine (TBM), developed within the past few decades, is designed to make the process of tunnel excavation safer and more economical. The use of TBMs in civil and mining construction projects is controlled by several factors including economic considerations and schedule deadlines. Hence, improved methods for estimating TBM performance are important for future projects. This paper presents a new model based on the group method of data handling (GMDH) for predicting the penetration rate (PR) of a TBM. In order to achieve this aim, after investigation of the most effective parameters of PR, rock quality designation, uniaxial compressive strength, rock mass rating, Brazilian tensile strength, weathering zone, thrust force per cutter and revolutions per minute were selected and measured to estimate TBM PR. A database composed of 209 datasets was prepared according to the mentioned model inputs and output. Then, based on the most influential factors of GMDH, a series of parametric investigations were carried out on the established database. In the following, five different datasets with different sets of training and testing were selected and used to construct GMDH models. Aside from that, five multiple regression (MR) models/equations were also proposed to predict TBM PR for comparison purposes. After that, a ranking system was used in order to evaluate the obtained results. As a result, performance prediction results of [i.e. coefficient of determination (R2) = 0.946 and 0.924, root mean square error (RMSE) = 0.141 and 0.169 for training and testing datasets, respectively] demonstrated a high accuracy level of GMDH model in estimating TBM PR. Although both methods are applicable for estimation of PR, GMDH is able to provide a higher degree of accuracy and can be introduced as a new model in this field.

Feasibility of ANFIS model for prediction of ground vibrations resulting from quarry blasting

Abstract: One of the most significant environmental issues of blasting operations is ground vibration, which can cause damage to the surrounding residents and structures. Hence, it is a major concern to predict and subsequently control the ground vibration due to blasting. This paper presents two artificial intelligence techniques, namely, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network for the prediction of ground vibration in quarry blasting site. For this purpose, blasting parameters as well as ground vibrations of 109 blasting operations were measured in ISB granite quarry, Johor, Malaysia. Moreover, an empirical equation was also proposed based on the measured data. Several AI-based models were trained and tested using the measured data to determine the optimum models. Each model involved two inputs (maximum charge per delay and distance from the blast-face) and one output (ground vibration). To control capacity performances of the predictive models, the values of root mean squared error (RMSE), value account for (VAF), and coefficient of determination (R2) were computed for each model. It was found that the ANFIS model can provide better performance capacity in predicting ground vibration in comparison with other predictive techniques. The values of 0.973, 0.987 and 97.345 for R2, RMSE and VAF, respectively, reveal that the ANFIS model is capable to predict ground vibration with high degree of accuracy.