Real-time model predictive control of a wastewater treatment plant based on machine learning.

Two separate goals should be jointly pursued in wastewater treatment: nutrient removal and energy conservation. An efficient controller performance should cope with process uncertainties, seasonal variations and process nonlinearities. This paper describes the design and testing of a model predictive controller (MPC) based on neuro-fuzzy techniques that is capable of estimating the main process variables and providing the right amount of aeration to achieve an efficient and economical operation.

Membrane bioreactor fouling behaviour assessment through principal component analysis and fuzzy clustering.

Adequate membrane bioreactor operation requires frequent evaluation of the membrane state. A data-driven approach based on principal component analysis (PCA) and fuzzy clustering extracting the necessary monitoring information solely out of transmembrane pressure data was investigated for this purpose. Out of three tested PCA techniques the two functional methods proved useful to cope with noise and outliers as opposed to the common standard PCA, while all of them presented similar capabilities for revealing data trends and patterns.

A fuzzy quality index for the environmental assessment of a restored wetland.

This paper describes the feasibility study for the restoration of agricultural land with a tendency to become waterlogged into a natural wetland, conceived to mitigate floods and to remove nutrients from the water drained from the cultivated plots. The wetland model, developed in aquatox, includes the nutrient dynamics both in the water and in the sediment, and the vegetation that is expected to develop as a consequence of flooding. The model inputs were synthesized from historical time series of rainfall and chemical data collected over the last decade.

Artificial intelligence control of a sequencing batch reactor for nitrogen removal via nitrite from landfill leachate.

Leachate generated in old landfills is a high-strength wastewater, which is particularly difficult to treat owing to its low biochemical oxygen demand/total Kjeldahl nitrogen ratio. This paper seeks to demonstrate that reliable leachate treatment by means of sequencing batch reactors (SBRs) is indeed possible by means of the application of a smart control system. This study assesses the results of a computer-controlled bench-scale SBR treating raw sanitary landfill leachate to achieve nitrogen removal through the nitrite shortcut.

Real-time fault detection and isolation in biological wastewater treatment plants.

Automatic fault detection is becoming increasingly important in wastewater treatment plant operation, given the stringent treatment standards and the need to protect the investment costs from the potential damage of an unchecked fault propagating through the plant. This paper describes the development of a real-time Fault Detection and Isolation (FDI) system based on an adaptive Principal Component Analysis (PCA) algorithm, used to compare the current plant operation with a correct performance model based on a reference data set and the output of three ion-specific sensors (Hach-Lange gmbh, Düsseldorf, Germany): two Nitratax NOx UV sensors, in the denitrification tank and downstream of the oxidation tanks, where an Amtax ammonium-N sensor was also installed. The algorithm was initially developed in the Matlab environment and then ported into the LabView 8.