Experimental Investigation of the Electrical Resistivity of Cement Dust

Electrical resistivity is one of the important particle–based factors influencing the performance of an Electrostatic Precipitator (ESP), a particulate control device commonly employed in most cement industries in Nigeria. Therefore, this study investigated the electrical resistivity of Cement Kiln Dust (CKD) across nine locally-operated cement manufacturing plants in Nigeria with the aim of tracing causes of performance problems associated with the ESP used for dust control in the plants. Samples of CKD were collected from the ESPs of these plants and tested for their resistance using the two probe method. The measured electrical resistivities were in the range of 108 – 1011 Ω·cm and showed strong dependence on temperature and slight variation with particle size. The CKD’s resistivity increases as temperature rises from ambient to about 250℃ and declines as temperature rises above 300℃; Nevertheless, the resistivities are adaptable for efficient ESP performance in the collection of cement dust

Gaseous Emission from the Combustion of Premium Motor Spirit (PMS) from the Kaduna Refinery and Petrochemical Company (KRPC) in Nigeria

Objectives : This study characterizes the gaseous emission from the combustion of PMS of different volumes from Kaduna Refinery and Petrochemical Company. Methods : The E8500 plus combustion analyzer was used for gaseous emission characterization of different volumes. Oxygen (O2), Hydrocarbons (HC), carbon monoxide (CO), carbon dioxide (CO2), Oxides of Nitrogen NOx (NO, NO2), Sulphur dioxide (SO2), and Hydrogen Sulfide (H2S) were measured using the analyzer. The values were recorded and the descriptive statistics graph was plotted. Results and Discussion : The concentrations for the gaseous emission from the combustion of PMS were 69.85 mg/m3 HC, 117.33 mg/m3 CO, 334 mg/m3 NOx for 10 ml, 58.93 mg/m3 HC, 130.33 mg/m3 CO, 784.33 mg/m3 NOx for 20 ml, 50.20 mg/m3 HC, 84.00 mg/m3 CO, 798.67 mg/m3 NOx for 30 ml, 65.48 mg/m3 HC, 160.33 mg/m3 CO, 850.33 mg/m3 NOx for 40 ml, 87.31 mg/m3 HC, 212.67 mg/m3 CO, 801.33 mg/m3 NOx, 3.67 mg/m3 SO2 for 50 ml. This study shows that CO and HC exceeded the permissible limit for stationary sources while NOx and SO2 were below the permissible limit. Conclusions : Hence, there is a need for rapid response and urgent attention from government and regulatory bodies to develop and implement appropriate policies that will help in reducing the effect. Appropriate measures to control air emissions that may be embarked upon by regulatory bodies include increase in the use of low-emission fuels and renewable fuels such as bio-fuels and introduction of the use of devices with low CO emissions

NEURO-FUZZY MODELLING OF BLENDING PROCESS IN CEMENT PLANT

The profitability of a cement plant depends largely on the efficient operation of the blending stage, therefore, there is a need to control the process at the blending stage in order to maintain the chemical composition of the raw mix near or at the desired value with minimum variance despite variation in the raw material composition. In this work, neuro-fuzzy model is developed for a dynamic behaviour of the system to predict the total carbonate content in the raw mix at different clay feed rates. The data used for parameter estimation and model validation was obtained from one of the cement plants in Nigeria. The data was pre-processed to remove outliers and filtered using smoothening technique in order to reveal its dynamic nature. Autoregressive exogenous (ARX) model was developed for comparison purpose. ARX model gave high root mean square error (RMSE) of 5.408 and 4.0199 for training and validation respectively. Poor fit resulting from ARX model is an indication of nonlinear nature of the process. However, both visual and statistical analyses on neuro-fuzzy (ANFIS) model gave a far better result. RMSE of training and validation are 0.28167 and 0.7436 respectively, and the sum of square error (SSE) and R-square are 39.6692 and 0.9969 respectively. All these are an indication of good performance of ANFIS model. This model can be used for control design of the process