A stepwise-cluster microbial biomass inference model in food waste composting

Journal Article · Mon Dec 14 23:00:00 EST 2009 · Waste Management

DOI:https://doi.org/10.1016/j.wasman.2009.06.023· OSTI ID:21357625

Wei, Sun ^[1]; Guangming, Zeng ^[2]; Xiaosheng, Qin ^[3]; Xueling, Sun ^[1]

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1. Faculty of Engineering, University of Regina, Regina, Saskatchewan, S4S 0A2 (Canada)
2. College of Environmental Science and Engineering, Hunan University, Changsha, Hunan 410082 (China)
3. School of Civil and Environmental Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798 (Singapore)

A stepwise-cluster microbial biomass inference (SMI) model was developed through introducing stepwise-cluster analysis (SCA) into composting process modeling to tackle the nonlinear relationships among state variables and microbial activities. The essence of SCA is to form a classification tree based on a series of cutting or mergence processes according to given statistical criteria. Eight runs of designed experiments in bench-scale reactors in a laboratory were constructed to demonstrate the feasibility of the proposed method. The results indicated that SMI could help establish a statistical relationship between state variables and composting microbial characteristics, where discrete and nonlinear complexities exist. Significance levels of cutting/merging were provided such that the accuracies of the developed forecasting trees were controllable. Through an attempted definition of input effects on the output in SMI, the effects of the state variables on thermophilic bacteria were ranged in a descending order as: Time (day) > moisture content (%) > ash content (%, dry) > Lower Temperature (deg. C) > pH > NH{sub 4}{sup +}-N (mg/Kg, dry) > Total N (%, dry) > Total C (%, dry); the effects on mesophilic bacteria were ordered as: Time > Upper Temperature (deg. C) > Total N > moisture content > NH{sub 4}{sup +}-N > Total C > pH. This study made the first attempt in applying SCA to mapping the nonlinear and discrete relationships in composting processes.

• OSTI ID: 21357625
• Journal Information: Waste Management, Journal Name: Waste Management Journal Issue: 12 Vol. 29; ISSN WAMAE2; ISSN 0956-053X
• Country of Publication: United States
• Language: English

Related Subjects

54 ENVIRONMENTAL SCIENCES
ASH CONTENT
BACTERIA
BIOLOGICAL MATERIALS
BIOLOGICAL WASTES
BIOMASS
CLASSIFICATION
COMPOSTING
ENERGY SOURCES
FEASIBILITY STUDIES
FOOD
FORECASTING
MANAGEMENT
MATERIALS
MICROORGANISMS
MOISTURE
NONLINEAR PROBLEMS
PH VALUE
PROCESSING
RENEWABLE ENERGY SOURCES
SIMULATION
WASTE MANAGEMENT
WASTE PROCESSING
WASTES