Search results for "ANAEROBIC MEMBRANE BIOREACTORS"
showing 3 items of 3 documents
Global sensitivity analysis of a filtration model for submerged anaerobic membrane bioreactors (AnMBR)
2014
The results of a global sensitivity analysis of a filtration model for submerged anaerobic MBRs (AnMBRs) are assessed in this paper. This study aimed to (1) identify the less- (or non-) influential factors of the model in order to facilitate model calibration and (2) validate the modelling approach (i.e. to determine the need for each of the proposed factors to be included in the model). The sensitivity analysis was conducted using a revised version of the Morris screening method. The dynamic simulations were conducted using long-term data obtained from an AnMBR plant fitted with industrial-scale hollow-fibre membranes. Of the 14 factors in the model, six were identified as influential, i.e…
Wastewater treatment and reclamation : A review of pulp and paper industry practices and opportunities
2016
The pulp and paper (P&P) industry worldwide has achieved substantial progress in treating both process water and wastewater, thus limiting the discharge of pollutants to receiving waters. This review covers a variety of wastewater treatment methods, which provide P&P companies with cost-effective ways to limit the release of biological or chemical oxygen demand, toxicity, solids, color, and other indicators of pollutant load. Conventional wastewater treatment systems, often comprising primary clarification followed by activated sludge processes, have been widely implemented in the P&P industry. Higher levels of pollutant removal can be achieved by supplementary treatments, which…
Model-based automatic tuning of a filtration control system for submerged anaerobic membrane bioreactors (AnMBR)
2014
This paper describes a model-based method to optimise filtration in submerged AnMBRs. The method is applied to an advanced knowledge-based control system and considers three statistical methods: (1) sensitivity analysis (Morris screening method) to identify an input subset for the advanced controller; (2) Monte Carlo method (trajectory-based random sampling) to find suitable initial values for the control inputs; and (3) optimisation algorithm (performing as a supervisory controller) to re-calibrate these control inputs in order to minimise plant operating costs. The model-based supervisory controller proposed allowed filtration to be optimised with low computational demands (about 5min). E…