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Landfill Settlement: Controlling the Time to Completion - WR0301

Description
Waste settlement prediction and monitoring are crucial to understanding and managing the lifecycle of a landfill. Settlement influences the progress of hydraulic and biodegradation processes, affects the performance of landfill infrastructure including the capping, cover materials, side slope liners and leachate and gas management systems, and is a key factor in assessing when landfill sites have stabilised (to the point that all management can be removed). Predicted settlements also determine the planned landfill profile on completion of filling and, therefore, the volume of fresh waste that can be disposed of at the landfill. The ability to predict waste settlement is important in the support of sustainable landfill management, an essential aim of the Waste and Resources R&D Strategy.

Current methods of settlement prediction have serious shortcomings in accounting for the organic (biodegradable) portion of waste streams and the many factors that control its decomposition; they are also unable to account for changing landfill conditions such as rates of filling or changes in waste type (as will occur from implementation of the Landfill Directive) that have major effects on settlement rates and magnitude. The existing methods are therefore difficult to use in a predictive manner and require recalibration for changing waste streams.

An alternative, more fundamental approach to landfill settlement has been developed from individually proven models of hydraulic, biodegradation and mechanical (HBM) behaviour. The public domain HBM model constitutes an innovative, integrated approach to landfill processes that is able to account for a wide range of behaviour and site management practice both during filling and post closure. For the full potential of the HBM model to be realised, field scale validation is necessary using input parameters acquired from actual landfill data obtained under normal operating conditions. A graphical user interface (GUI) has also been developed which provides a user-friendly Windows environment and help menu in which landfill simulations can be quickly and accurately prepared.

The research project has the key objectives of:

• the evaluation and refinement of the HBM model using actual landfill data sets; and
• the development of a guidance document on the practical prediction of landfill settlement.

Objective
The principal aim of the project is the establishment of a model and accompanying protocol by which to analyse, interpret and predict the magnitude and time to completion of long-term landfill settlement in relation to (i) fundamental factors, such as moisture content, degradable fraction and degradability and (ii) site operations such as, filling sequence, compactive effort, leachate management and capping.

The project aim will be delivered through three main work phases, corresponding to the following three general objectives. These are:

GO1) Assembly and evaluation of long term landfill settlement data sets and compilation of selected suitable data sets for model validation.

GO2) A critical evaluation of the operation and quantitative performance of the HBM model against long-term landfill settlement data and refinement of the model as necessary.

GO3) Production of landfill settlement monitoring and prediction protocol.

Project Documents
• Final Report : Landfill Settlement: Controlling the time to completion   (498k)
• Final Report : Landfill Settlement: Controlling the time to completion   (3185k)
Time-Scale and Cost
From: 2006

To: 2008

Cost: £76,450
Contractor / Funded Organisations
Golder Associates (UK) Limited
Keywords
Analysis              
Benchmarking              
Environmental Protection              
Forecasting              
Managing residual waste in landfill              
Monitoring and evaluation              
Residual Wastes Management              
Techniques & methodolgies for waste management              
Waste              
Waste Management              
Fields of Study
Waste Management