Statistical modelling for the prediction and control of PCDDs and PCDFs emissions from municipal solid waste incinerators |
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Affiliation: | 1. VTT Technical Research Centre of Finland, Koivurannantie 1 PL 1603, 40 101 Jyväskylä, Finland;2. Department of Chemistry, Renewable Natural Resources and Chemistry of Living Environment, University of Jyväskylä, POB 35, FI-40014 Jyväskylä, Finland;3. Valmet Power, Lentokentänkatu 11, FI-33101 Tampere, Finland;4. Stora Enso Newsprint and Book Paper, Anjalankoski Mills, FI 46900 Anjalankoski, Finland;1. School of Mechanical Engineering, VIT University, Vellore 14, Tamil Nadu, India;2. Department of Management Engineering, Technical University of Denmark, Lyngby, Denmark;1. School of Electric Power, South China University of Technology, 510640, Guangzhou, China;2. Guangdong Province Key Laboratory of Efficient and Clean Energy Utilization, 510640, Guangzhou, China;1. The School of Energy and Power Engineering, Shandong University, Jinan, 250061, China;2. State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, 100084, China;1. College of Materials and Energy, South China Agricultural University, Guangzhou 510642, China;2. School of Electric Power, South China University of Technology, Guangzhou 510640, China |
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Abstract: | The emissions of a full range of polychlorinated dibenzo-p-dioxins and furans (PCDDs/PCDFs) have become one of the most controversial issues in siting and building new municipal incinerators. The lack of comprehensive evaluation of the PCDDs/PCDFs formation and emission control technologies in earlier times resulted in ambiguity in the decision-making of incineration projects. Until the last decade, several emission tests of municipal solid waste incineration regarding to new combustion criteria, public regulations, and risk assessments were conducted and reported in the literature. However, only a few analyses of statistical prediction and control have been established. This paper presents a series of multiple linear regression models for PCDDs/PCDFs emission prediction and control corresponding to different types of incinerators. The data used in the regression analysis were integrated from several testing programmes held in North America. By applying these regression results, evaluation of various combustion criteria, public regulations, and environmental and health risk assessment can then be achieved. Such evaluation is valuable for some developing countries which do not have sufficient finance or engineering experience to pursue large scale emission tests, but need rapid promulgation of emissions control for municipal solid waste incinerators. |
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