Performance assessment of multi-input-single-output (MISO) production process using transfer function and fuzzy logic: A case study of soap production

In this research an improved and novel method of assessing the performance of multi input single output (MISO) processes, as typified by soap production process was studied. The method involves the combination of transfer function and fuzzy logic and was used in assessing the three years performance of a soap factory. A comparison of the years studied shows that the year 2011 with a performance rating λ of 0.761 which corresponds to the linguistic variable “Good” recorded the best performance, while the year 2012 with a performance rating λ of 0.250 which corresponds to the linguistic variable “Poor” recorded the worst performance. The result of this study will help to improve maintenance effectiveness, quality, utilization of raw materials and efficiency of MISO production processes

Predicting the Compressive Strength of Concretes Made with Unwashed Gravel from Eastern Nigeria Using Artificial Neural Networks

Cases of collapsed buildings and structures are prevalent in Nigeria. In most of these cases the cause of the collapse could be traced to the strength of the construction materials, mainly concrete. Secondly, experimental determination of the strength of concrete materials used in buildings and structures is quite expensive and time consuming. This research seeks to develop a computational model based on artificial neural networks for the determination of the compressive strength of concrete materials made from a prevalent coarse aggregate component from Nigeria. The study involved building a multilayer perceptron neural network model which was trained using experimental data obtained from compressive strength test of concrete made from unwashed gravel. Compressive strength predictions were compared with alternative model based on regression analysis. Results show that for the unwashed gravel based concrete the regression model prediction has a sum of squares error of 9.808 and a mean absolute percentage (relative) error of 1.167, while the neural network model prediction has a sum of squares error of 0.025 and a mean absolute percentage (relative) error of 0.015. Generally, the models predicted well, but the neural network model predicted better than the regression model. This study has ably demonstrated a cheap, simple, very quick and accurate alternative to experimental method of concrete strength determination. It is simpler and quicker than analytical methods based on regression analysis. Keywords: Artificial Neural Network, Concrete, Unwashed gravel, Regression, ModellingNigerian Journal of Technological Research, 18(2), 201

Age hardening process modeling and optimization of aluminum alloy A356/Cow horn particulate composite for brake drum application using RSM, ANN and simulated annealing

Most conventional ceramic based aluminum metal matrix composites (MMCs) are either heavy, costly or combination of both. In order to reduce cost and weight, while at the same time maintaining quality, cow horn particles (CHp) was used with aluminum alloy A356 to produce MMC for brake drum application and other engineering uses. The aim of this research is to model the age hardening process of the produced composite using response surface methodology (RSM) and artificial neural network (ANN), and to use the developed ANN as fitness function for a simulated annealing optimization algorithm (SA-NN system) for optimization of age hardening process parameters. The results show that ANN modeled the age hardening data excellently and better than RSM with a correlation coefficient of experimental response with ANN predictions being 0.9921 as against 0.9583 for the RSM. The SA-NN system optimized process parameters were in very close agreement with the experimental values with the maximum relative error of 1.2%, minimum of 0.35% and average of 0.71%. Keywords: Artificial neural network, Response surface methodology, Simulated annealing, Age hardening, Metal matrix composit

Performance appraisal of gas based electric power generation system using transfer function modelling

Gas flaring for years has been a major environmental problem in many parts of the world. One way of solving the problem of gas flaring is to effectively utilize the abundant supply of gas for power generation. To effectively utilize gas for power generation requires highly efficient gas turbines and power facilities. Traditional methods of assessing the efficiency of power generation turbines do not take into consideration the stochastic nature of gas input and power output. This is because in a power generation system, as in any typical production system, there is generally marked variability in both input (gas) and output (power) of the process. This makes the determination of the relationship between input and output quite complex. This work utilized Box-Jenkins transfer function modelling technique, an integral part of statistical principle of time series analysis to model the efficiency of a gas power plant. This improved way of determining the efficiency of gas power generation facilities involves taking input–output data from a gas power generation process over a 10-year period and developing transfer function models of the process for the ten years, which are used as performance indicators. Based on the performance indicators obtained from the models, the results show that the efficiency of the gas power generation facility was best in the years 2007–2011 with a coefficient of performance of 0.002343345. Similarly, with a coefficient of performance of 0.002073617, plant performance/efficiency was worst in the years 2002–2006. Using the traditional method of calculating efficiency the values of 0.2613 and 0.2516 were obtained for years 2002–2006 and 2007–2011 respectively. The result is remarkable because given the state of the facilities, it correctly predicted the period of expected high system performance i.e. 2002–2006 period, but the traditional efficiency measurement method failed to do so. Ordinarily, using efficiency values obtained through the traditional method as the metric, the system managers would assume that the period 2002–2006 was better than in the period 2007–2011 whereas the reverse is the case. The result of this study is expected to open new ways to improving maintenance effectiveness and efficiency of gas power generation facilities

Performance evaluation of multi-input–single-output (MISO) production process using transfer function and fuzzy logic: Case study of a brewery

This work reports an improved and novel new method of evaluating the performance of multi input single output (MISO) processes, as exemplified by a brewery. This new method involves the combination of transfer function modeling and fuzzy logic and was used in evaluating the six years performance of a brewery. Of the six years, the period 2010–2011 with a performance rating λ of 0.810 which corresponds to the linguistic variable ‘Good’ recorded the best performance while the period 2008–2009 with a performance rating λ of 0.381 which corresponds to the linguistic variable ‘Fair’ recorded the worst performance. The result of this study is expected to open new ways of improving maintenance effectiveness, utilization of raw materials and efficiency of multi input single output (MISO) production processes

Modelling the impact of intervention measures on total accident cases in Nigeria using Box-Jenkins methodology: A case study of federal road safety commission

Road traffic accidents (RTA) have been a very big problem in many developing countries including Nigeria, causing many deaths and disabilities. The aim of this research is to model the effects of intervention measures adopted by the Nigerian government in curbing RTA. In the research road traffic accident data from 1960 to 2014 were analyzed using Box-Jenkins intervention methodology. The result of the modeling and analysis showed that the establishment of Federal Road Safety Commission in 1987, a Nigerian Government intervention measure, had an abrupt temporary impact on RTA in Nigeria (ω0 = −2,423). The findings also showed that the total number of accident cases in Nigeria from 1961 to 1987 (26 years), the period before the intervention, was 657,280 while the total number of accident cases from 1988 to 2014 (26 years), the period after the intervention, was 430,721. This represents a 34.5% reduction in total accident cases after the intervention. In terms of accident density there was a 67.4% reduction in accident density during the post intervention period under consideration. It can be concluded that the establishment of the road safety agency has a positive impact on total cases of RTA in Nigeria by reducing it significantly, although RTA still continues to be a big problem in Nigeria. This model and analysis will assist road safety agencies to re-strategize in their policy implementation in order to further reduce RTA occurrence, the number of persons killed and injured in Nigeria