Nonlinear time-series modelling for prediction of water quality of wastewater treatment processes




København og omegn

DTU Chemical Engineering and Danish Hydraulic Institute (DHI) are looking for a Master student interested in data analysis and mathematical modelling in wastewater treatment engineering.


Biological processes in wastewater often exhibit time-varying and highly dynamic characteristics, which can be influenced by various known and unknown factors.  Water quality can vary depending on concentration of different substrates and pollutants, the oxygen level, the solids retention time, pH, temperature and many more. Various mathematical models have been developed to evaluate and diagnose the performance of wastewater treatment plants (WWTPs). The dynamic models are primarily based on first principles modelling approaches where physico-chemical and biological processes are often described through sets of differential or algebraic equations to simulate the variation of model components (e.g. concentration of different pollutants) with respect to time. These models are usetful for process simulation, optimization and control. Alternatively, Machine learning (ML) techniques can yield models that have shown great potential in predicting the behavior of complicated biological systems with high accuracy. The main advantage of such models is that they can predict the output values based on input values after training and evaluation steps, without the requirement of having a complex physical calibrated model. Hence, these models can demonstrate the cause-and-effect relationship between input and output values that can ultimately be helpful for process dynamics understanding and as a decision-making tool. One of the main obstacles in achieving satisfactory predictions with ML models is the scarcity of available data and lthe ack of accurate measurements. This is a frequent problem in making decisions relating to operational costs on WWTPs. To keep the monitoring cost low and yet have satisfactory ML model predictions, the type, frequency and the location of measurements should be well adjusted.

Project objectives:

-  Perform dynamic simulations (in WEST by DHI) to predict targeted effluent concentrations (e.g. total nitrogen and COD) in a benchmark simulation, considering default settings of the model.

-          Develop machine learning (ML) models, in Python, R, or Matlab (e.g. artificial neural networks and support vector machines) to predict  the targeted effluent concentrations based on a set of already simulated components (e.g. TSS, COD, total nitrogen, total phosphorous, temperature and pH) at different locations (e.g. influent, aeration tank and sedimentation).

-          Assess ML model performance against WEST predictions based on well-defined criteria

-          Sensitivity analysis using ML model to identify the input parameters that may have potential influence on the prediction of targeted effluent concentrations.

-          Test the ML prediction accuracy under different scenarios by changing: i - frequency of data; ii - precision of data e.g. considering an uncertainty with normal distribution; iii - location of data (i.e. change location where data are collected in teh WWTP). 

I samarbejde med

Pedram Ramin (DHI) (

Søg i opslag


DTU Kemiteknik


Krist V. Gernaey





Kandidatuddannelsen i Kemisk og Biokemisk Teknologi


Krist V. Gernaey


30 - 35




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Anker Engelunds Vej 1
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2800 Kgs. Lyngby

45 25 25 25

CVR-nr. 30 06 09 46

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