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                <text>The primary objective of this paper was to investigates the use and benefits of simulation for basic instrumentation and process control education. This simulation approach provide valuable solution by giving clear insights into complex systems. A graphic computer simulation program, for controlling and neutralising acidic wastewater has been developed. The simulator (pH-neutraliser) was developed using Microsoft Visual Basic Programming language and process control principles. The program was designed to provide an intuitive interface by correcting and adjusting the acidic pH of wastewater to 7 (neutral pH) before discharge into the environment. The acidic wastewater can be neutralised by alkali reagents like caustic soda or lime. The simulator was designed to operate in both manual and automatic modes during the neutralisation process. In the manual mode, the pH-neutraliser alerts an operator by sounding an alarm and suggests the percentage by which the alkali reagent valve should be opened to add required amount to bring the pH of the wastewater to 7. This is done when the measured value from the pH sensor and transmitter is not equal to the desired value. In the automatic mode, the neutraliser is expected to automatically open the alkali reagent valve to the required percentage to add the needed amount of alkali to make the pH of wastewater neutral. The pH neutralisation automation of wastewater can be achieved with the aid of pH analytical sensor and transmitter, signals, controller and feedback control mechanism.</text>
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                <text>In this study, we explore both univariate and multivariate aspects of time series analysis. In the univariate aspect, we evaluated the predictive performance of three widely used univariate time series methods in forecasting the electricity consumption in Ghana during the 1980–2011 periods. The three univariate time series approaches are autoregressive integrated moving average (ARIMA), autoregressive fractional integrated moving average (ARFIMA) and exponential smoothing. In each approach, we examined competing models and the “best” model according to the minimum information criterion and diagnostic checking was selected. The forecast accuracy measure (ie; mean absolute forecast error, MAFE) was computed for each “best” model in the three different approaches. The empirical results revealed that the MAFE for ARIMA, ARFIMA and exponential smoothing were 31.3%, 9.4% and 41.6% respectively. Thus, the comparative analysis of the forecast performance of these methods clearly concluded that the ARFIMA method gives better forecast in predicting electricity consumption in Ghana. And, in the multivariate aspect, we examined whether GDP, export, import and population influences electricity consumption. The results revealed a feedback causality between electricity consumption and economic growth. Again, we established that there exists an uni-directional influence of import, export, population towards electricity consumption. The “best” model of the univariate approach is ARFIMA (2, 0.31, 1) with MAFE of 9.4% while the “best” model for the multivariate approach is vector error correction model VECM (3) with MAFE of 1.5 …</text>
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