Industrial and Manufacturing Engineering Publications

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Industrial and Manufacturing Engineering Publications

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Browsing Industrial and Manufacturing Engineering Publications by Author "Mugwagwa, L."

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    Air pollution control techniques for the cement manufacturing industry: A case study for Zimbabbwe
    (2012) Zimwara, D.; Mugwagwa, L.; Chikowore, T.R.
    Technological advancement has resulted in cement making companies being able to produce higher volumes compared to the past. However the higher production levels have also been largely labelled as the leading cause of pollution. The main sources of air pollution in the industry include excavation activities, dumps, tips, conveyer belts, crushing mills and kiln emissions. Harnessing appropriate technology for use in the cement industry could go a long way towards minimising on-site wastes and pollution. This review examines various options in practice for reducing pollution at cement manufacturing companies, which help ensure legislative compliance. By adoption of appropriate technology and computer modelling, industry will not only reduce production waste but also comply with legislation to do with environmental protection. The paper examines certain methods of pollution control used for air and looks at how computer modelling can be adopted for the classification, quantification and control of particulate matter; and how efficient energy use can contribute to better air quality. An analysis of gas stack emissions was done for a cement manufacturing company in Zimbabwe where compliance was investigated. Emissions samples were randomly selected at various points within the company and concentration of various emission constituents were analysed.
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    Cost of Quality as a Driver for Continuous Improvement - Case Study – Company X
    (2013) Zimwara, D.; Mugwagwa, L.; Maringa, D.; Mnkandla, A.; Mugwagwa, L.; Ngwarati, T.T.
    In the manufacturing, metal casting industry is one of the oldest basic principal and most important industries. The casting process is hindered by the occurrence of various defects. High casting reject levels and customer returns have a considerable adverse effect on productivity, delivery performance, customer satisfaction and employee morale. In addition excessive rejection reduces yield, wastes valuable raw materials and involves management time in problem solving. All foundry processes generate a certain level of rejection that is closely related to the type of casting, the processes used and the equipment available. This paper seeks to establish the extent to which cost of quality can impact on continuous improvement of the products and the relationship with the customers of the organization. To determine the cost of quality (COQ) at casting company X, the researchers used existing company records, publications and historical evidence of the company. The researchers utilized techniques such as, bar charts and tables in presenting and interpreting data. The above techniques have the ability to provide methods for collecting, presenting, and analysis and meaningfully interpret data. The research findings estimated the COQ to be 6.6% of sales revenue.
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    Soft Computing Methods for Predicting Environmental Quality: A Case Study of the Zimbabwe Sugar Processing Industry
    (2013) Zimwara, D.; Mugwagwa, L.; Nherera, K.
    Sugarcane growing and processing is associated with environmental degradation and pollution. The impact that sugar processing industries have on the environment affects the ecosystem. Methods of soft computing that is fuzzy logic, neural networks, and genetic algorithms can be adopted for environmental protection, particularly in the developing countries. Soft computing techniques, particularly neural networks and fuzzy logic, have been used to predict and sometimes control air quality. This paper looks at how fuzzy logic can be adopted for predicting air quality. The common environmental impacts associated with sugarcane production are water and air pollution. This paper focuses on air pollution. The major waste streams are identified and the extent of air pollution is predicted by classifying the air quality as poor, ordinary, very good, and excellent. This paper presents a fuzzy rule base that can be used to classify the pollutants and predict the air quality based on the amount of the specific pollutant in the air. The Mamdani fuzzy inference system is used to build the rule base, with the membership functions being non-intersecting and triangular. The adoption of fuzzy logic techniques will help sugar processing industries to be aware of the impact their operations have on the environment.