Expert Systems in Solid Waste Management

Basri, Hassan; Stentiford, E. I.

doi:10.1177/0734242x9501300108

Cited by 14 publications

(7 citation statements)

References 22 publications

(22 reference statements)

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“…The modern waste management models found in the literature incorporate expert systems [10][11][12], evolutionary programming [13], artificial neural networks [14][15][16][17][18], multiple linear regression (MLR) [14,19,20] central composite design (CCD) [14] and various combination of these tools. It is, however, artificial neural network (ANN) modeling that has been on the forefront due to its distinct advantages over other methods, such as the clear network model, uncomplicated implementation and the quality of performance [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Municipal Waste Generation in Poland Using Neural Network Modeling

Kulisz

Kujawska

2020

Sustainability

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Planning is a crucial component of short- and long-term municipal waste management. Establishing the relationships between the factors that determine the amount of waste generated by municipalities and forecasting the waste management needs plays a fundamental role in the development of effective planning strategies and implementation of sustainable development. Artificial Neural Network employed for verifying the forecasts pertaining to the amount of rainfall in Poland were presented in the studies. The proposed models included selected explanatory indices in order to reflect the impact of social, demographic and economic factors on the amount of generated waste. Mean squared error (MSE) and regression value (R) are used as indices of efficiency of the developed models. The ANN models exhibited high accuracy of forecasts at high R values (R = 0.914, R = 0.989) and low MSE values. Derived from the socioeconomic data for 2003–2019, the model predicts that the future waste generation in 2024 will increase by 2%. The results indicate that the employed ANN models are effective in predicting the amount of waste and can be considered a cost-effective approach to planning integrated waste management systems.

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Section: Introductionmentioning

confidence: 99%

Prediction of Municipal Waste Generation in Poland Using Neural Network Modeling

Kulisz

Kujawska

2020

Sustainability

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“…The system eventually reaches a conclusion and gives a statement saying that the site is suitable or not suitable for a landfill. The user can then view the explanation for the system's reasoning process and the knowledge-base items that were utilized to arrive at the conclusion (Basri & Stentiford, 1995).…”

Section: Information Retrieval Systemsmentioning

confidence: 99%

Computer applications in environmental data analysis

Zafar

2004

Environmental Quality Mgmt

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The aim of our study was to develop a magnetic resonance (MR)‐compatible in vitro model containing freshly isolated rat hepatocytes to study the transport of hepatobiliary contrast agents (CA) by MR imaging (MRI). We set up a perfusion system including a perfusion circuit, a heating device, an oxygenator, and a hollow fiber bioreactor (HFB). The role of the porosity and surface of the hollow fiber (HF) as well as the perfusate flow rate applied on the diffusion of CAs and O2 was determined. Hepatocytes were isolated and injected in the extracapillary space of the HFB (4 × 107 cells/mL). The hepatocyte HFB was perfused with an extracellular CA, gadopentetate dimeglumine (Gd‐DTPA), and gadobenate dimeglumine (Gd‐BOPTA), which also enters into hepatocytes. The HFB was imaged in the MR room using a dynamic T1‐weighed sequence. No adsorption of CAs was detected in the perfusion system without hepatocytes. The use of a membrane with a high porosity (0.5 μm) and surface (420 cm2), and a high flow rate perfusion (100 mL/min) resulted in a rapid filling of the HFB with CAs. The cellular viability of hepatocytes in the HFB was greater than 85% and the O2 consumption was maintained over the experimental period. The kinetics of MR signal intensity (SI) clearly showed the different behavior of Gd‐BOPTA that enters into hepatocytes and Gd‐DTPA that remains extracellular. Thus, these results show that our newly developed in vitro model is an interesting tool to investigate the transport kinetics of hepatobiliary CAs by measuring the MR SI over time. © 2004 Wiley Periodicals, Inc.

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“…They are a logical way to handle landfill design because of the importance of practical knowledge, the multidisciplinary nature of design, and the limited access to expert opinion in some regions. Reports of ESs that failed to be implemented in practice highlight the problems of attempting to cover too wide of a scope, requiring too many input parameters, and lacking an adequate knowledge base (Basri and Stentiford, 1995).…”

Section: Introductionmentioning

confidence: 99%

A framework for a decision support system for municipal solid waste landfill design

Verge¹,

Rowe

2013

Waste Manag Res

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A decision support system (Landfill Advisor or LFAdvisor) was developed to integrate current knowledge of barrier systems into a computer application to assist in landfill design. The program was developed in Visual Basic and includes an integrated database to store information. LFAdvisor presents the choices available for each liner component (e.g. leachate collection system, geomembrane liner, clay liners) and provides advice on their suitability for different situations related to municipal solid waste landfills (e.g. final cover, base liner, lagoon liner). Unique to LFAdvisor, the service life of each engineered component is estimated based on results from the latest research. LFAdvisor considers the interactions between liner components, operating conditions, and the existing site environment. LFAdvisor can be used in the initial stage of design to give designers a good idea of what liner components will likely be required, while alerting them to issues that are likely to arise. A systems approach is taken to landfill design with the ultimate goal of maximising long-term performance and service life.

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Expert Systems in Solid Waste Management

Cited by 14 publications

References 22 publications

Prediction of Municipal Waste Generation in Poland Using Neural Network Modeling

Prediction of Municipal Waste Generation in Poland Using Neural Network Modeling

Computer applications in environmental data analysis

A framework for a decision support system for municipal solid waste landfill design

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