Prediction of Municipal Solid Waste Generation Models - Engineering Assignment Help

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Abstract  

 Development of a Municipal Solid Waste Management (MSWM) plan is a complex process. As a foundation and  prerequisite for efficient MSWM plan, quantification and prediction of Solid Waste (SW) generation is very much essentials.  Municipal Solid Waste (MSW) prediction cannot be done directly and depends on so many factors. In actual practices, due to  uncertainties and unavailability of sufficient data, modelling methods are needed for prediction of MSW generation. A number of researchers have predicted SW generation using various modeling methods. The main objective of this paper is to review such  models related to MSW generation using economic, socio-demographic or management-orientated data and identify possible  factors that will help in selecting the crucial design options within the framework of mathematical modeling. Five characteristic  classification criteria, namely, modeling method, area covered, time series, independent variables and waste streams are focused in this review. The entire published models are diverse in nature for application from whole country to households. Successful  modeling depends significantly on selection of waste stream. From the review and discussion of models the research aims to  identify the limitations of previous models which will help in identifying the crucial design options within the framework of  modelin.

 Introduction  

Solid Waste Management (SWM) is now one of the challenging issues for modern societies due to change in  consumption pattern and uncontrolled urbanization and industrialization. Municipal Solid Waste (MSW) includes  household, commercial, institutional, street sweeping, construction and demolition, and sanitation waste. MSW also  contains recyclables (paper, plastic, glass, metals, etc.), toxic substances (paints, pesticides, used batteries,  medicines), compostable organic matter (fruit and vegetable peels, food waste) and soiled waste (blood stained  cotton, sanitary napkins, disposable syringes) (Sharholy et al., 2008).  

 Global Waste Management Market Assessment (2007), reported 2.02 billion tones MSW generation globally  and annual increase rate of 8%. In India increasing urbanization and changing life styles, accelerate MSW  generation in cities eight times more MSW than they did in 1947. About 90 million tons of MSW were generated  annually (Sharholy et al., 2008). Per capita MSW generated rate increased to 1–1.33% annually (Bhide and Shekdar,  1998; Shekdar, 1999; Pappu et al., 2007). The composition and the quantity of MSW generated in India differ  greatly with that in the western countries (Jalan and Srivastava, 1995; Shannigrahi et al., 1997; Gupta et al., 1998)  particularly with hazards characteristics. Sharholy et al. (2008) mentioned that, MSW in urban areas contained large  fraction of compostable materials (40–60%) and inert (30–50%). The relative percentage of organic waste in MSW  was generally increasing with decrease in socio-economic status; so rural households generate more organic waste  than urban households.  

 It has been noticed that the physical and chemical components of MSW depends upon a number of factors such  as food habits, standard of living, degree of commercial activities, seasons etc. where the total MSW generation  depends on total population. Effective collection and proper disposal of MSW depends greatly upon accurate  prediction of generation of solid waste (Chang and Lin, 1997). MSW prediction cannot be made directly and  depends on many qualitative and quantitative factors. Due to uncertainties and insufficient data availability,  modeling methods were found to be beneficial.  

The main aim of this paper is to review the published models related to prediction of MSW generation. The  limitations of the previous models were also discussed to identify the crucial design options within the framework of  modeling.  

2. Study of MSW Prediction Models  

 Systematic reviews of various models on MSW prediction in this model may be regarded as an extension work  of Beigl et al. (2008). The models related to waste generation upto 2005 were included in his work. This paper  reviewed 20 MSW generation prediction models from 2006 to 2014. The waste generation prediction models are  mainly based on decision –support system such as cost benefit analysis, multicriteria decision analysis and life cycle  analysis. The reviewed models may be classified into five broad categories based on: modeling method, study area,  time series, independent variables considered and waste streams. 

 Modeling Methods  

 Conventional waste generation prediction models including correlation and regression models generally used  demographic and socioeconomic factors. Various independent variables were considered in most of the prediction  models. However, a grey fuzzy dynamic model developed by Chen and Chang, (2000) was not based on any  independent variable (except the time series data with at least three values). Some models used bivariate analysis  (only one independent variable) whose validation depends on real MSW data such as correlation and regression  analyses, time series analyses, and group comparison. These models expressed only cause and effect.  

 Other models used multivariate analysis (more independent variables) such as input output analysis, system  dynamics, artificial intelligent system (fuzzy logic, artificial neural network, genetic algorithms) and multiple  regression methods. These modeling methods create complications due to diverse interactions with the variables. As  a result validation of model becomes difficult. The models which were generally used to predict MSW generation  within 2006-2014 are support vector machine (Abbasi et al. 2012), wavelet transform (Noori et al. 2009; Abbasi et  al. 2012), artificial neural network (Noori et al. 2009; Abdoli et al. 2011; Antanasijevic et al. 2013), system dynamic  (Kollikkathara et al. 2010;Chen et al. 2012), multiple regression analysis ( Shan 2010; Dai et al. 2011; Keser et al.  2012), fuzzy logic (Karadimas and Orsoni, 2006; Lozano-Olvera et al. 2008; Oumarou et al. 2012), geographical  information system (Purcell and Magette, 2009; Keser et al. 2012), single regression analysis (Ojeda-Benitez et al.  2008; Thanh et al. 2010; Lebersorger and Beigl, 2011; Li et al.2011),

analytic hierarchy process (Li et al.2011), gray  model (Liu and Yu, 2007) and time series analysis (Owusu-Sekyere et al. 2013; Mwenda et al. 2014). Consideration  of large number of independent variables may increase cost of study due to requirement of large number of samples  and long continuous database. Therefore, it is essential to identify the data driven model (Noori et al., 2009). Among  all the method mentioned fuzzy modeling handled the linguistic terms. Hence this method can work with  quantitative and qualitative data. Fuzzy modeling approach is based on fuzzy rules which generate knowledge from  data set and these fuzzy rules transfer knowledge to take proper decision. Also fuzzy logic system handles  uncertainty of waste generation, insufficient dataset of samples and analysis of MSW successfully (Chen and Chang,  2000). Percentage wise, application of each modelling methods in 20 reviewed papers are presented in Figure

 
2.2 Study Area  

The study area considered by the modeling methods for prediction of MSW generation were mainly households and  administrative units of districts since data were readily available and the application areas were comparatively  smaller. Country wise data was also used in some models. 

In case of household related studies, relationship between individual habits and waste quantity, individual  characteristics of the representatives of the household itself were analyzed. Depending on house type like single  household, dwelling or an entire community, the sample sizes were ranged in between 50 to 100. MSW generation  predication was based, mainly on income level (Lozano-Olvera et al., 2008; Ojeda-Benitez et al. 2008). However,  weak correlation between income and household waste generation were also observed (Thanh et al. 2010). Thanh et  al. 2010 revealed MSW generation was based on population density level of a household. Most of researcher  acquired data through personal interviews and surveys. Individual level database were not available due to data  protection issues of census (Lebersorger and Beigl, 2003). Hence prediction of MSW generation of a household In MSW prediction modeling the term districts includes the federal states (Purcell and Magette, 2009; Dai et al.  2011) or city (Liu and Yu, 2007; Noori et al., 2009; Kollikkathara et al., 2010; Shan 2010; Abdoli et al. 2011; Li et  al., 2011; Chen et al. 2012; Owusu-Sekyere et al. 2013; Mwenda et al. 2014), city district, municipality and even  part of city Area (Oumarou et al. 2012). Modeling is not just restricted to a particular city. But it covered also a  significant number of small to medium sized municipalities (Lozano-Olvera et al., 2008; Lebersorger and Beigl,  2011; Abbasi et al. 2012), even electoral districts were considered (Purcell and Magette, 2009). Many studies covers  annual time series length (Liu and Yu, 2007; Kollikkathara et al., 2010; Shan 2010; Dai et al. 2011; Lebersorger and  Beigl, 2011; Li et al., 2011; Chen et al. 2012; Owusu-Sekyere et al. 2013), monthly, weekly and day wise (Lozano Olvera et al., 2008; Noori et al., 2009; Purcell and Magette, 2009; Abdoli et al. 2011; Abbasi et al. 2012; Mwenda et  al. 2014) data.  

3. Conclusions  

 Prediction of MSW generation plays a vital role in MSW management. A review of models on solid waste  generation predictions showed that the overall size of the household, income level of households, and the level of  education are most common attributes affecting the generation of waste. There is lack of official historical records of  attributes affecting solid waste generation (both qualitative and quantitative) especially in developing countries.  Since level of association between each of these attributes are not always same. So predictor in one level need not  necessarily be a predictor in another. These are the main limitation on prediction of MSW generation. The entire  published models are diverse in nature for application from county to households. Successful modeling is dependent  significantly on selection of waste stream. Most of the models were based on correlation and regression analysis.  Very few attempts have been made on artificial intelligent systems like fuzzy logic. 

         
 
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