Jani Tomperi

Real-time optical monitoring of the wastewater treatment process

One activated sludge process line was optically monitored in situ by a novel image analysis equipment. The results of the image analysis were studied to find out dependencies to the process variables of the wastewater treatment plant (WWTP) and to the quality of the treated wastewater. The quality parameter of the treated wastewater, suspended solids, was modelled using the image analysis results. The model can be used for evaluating the performance of the WWTP and for the better control for stable effluent quality. It was shown that the results of the online optical monitoring reveal useful information from the process and can be used in forecasting the quality of biologically treated wastewater. The optical monitoring method together with process measurements has an important role in keeping the process in stable operating conditions and avoiding environmental risks.

Modelling effluent quality based on a real-time optical monitoring of the wastewater treatment process

ABSTRACT A novel optical monitoring device was used for imaging an activated sludge process in situ during a period of over one year. In this study, the dependencies between the results of image analysis and the process measurements were studied, and the optical monitoring results were utilized to predict the important quality parameters for the wastewater treatment process efficiency: suspended solids, biological oxygen demand, chemical oxygen demand, total nitrogen and total phosphorous in biologically treated wastewater. The optimal subsets of variables for each model were searched using five variable selection methods. It was shown that online optical analysis results have clear dependencies on some process variables and the purification result. The model based on optical monitoring and process variables from the early stage of the treatment process can be used to predict the levels of important quality parameters, and to show the quality of the biologically treated wastewater hours in advance. This study confirms that the optical monitoring method is a valuable tool for monitoring a wastewater treatment process and receiving new information in real time. Combined with predictive modelling, it has the potential to be used in process control, keeping the process in a stable operating condition and avoiding environmental risks.

Early warning of changing drinking water quality by trend analysis

Monitoring and control of water treatment plants play an essential role in ensuring high quality drinking water and avoiding health-related problems or economic losses. The most common quality variables, which can be used also for assessing the efficiency of the water treatment process, are turbidity and residual levels of coagulation and disinfection chemicals. In the present study, the trend indices are developed from scaled measurements to detect warning signs of changes in the quality variables of drinking water and some operating condition variables that strongly affect water quality. The scaling is based on monotonically increasing nonlinear functions, which are generated with generalized norms and moments. Triangular episodes are classified with the trend index and its derivative. Deviation indices are used to assess the severity of situations. The study shows the potential of the described trend analysis as a predictive monitoring tool, as it provides an advantage over the traditional manual inspection of variables by detecting changes in water quality and giving early warnings.

Drinking water quality monitoring using trend analysis

One of the common quality parameters for drinking water is residual aluminium. High doses of residual aluminium in drinking water or water used in the food industry have been proved to be at least a minor health risk or even to increase the risk of more serious health effects, and cause economic losses to the water treatment plant. In this study, the trend index is developed from scaled measurement data to detect a warning of changes in residual aluminium level in drinking water. The scaling is based on monotonously increasing, non-linear functions, which are generated with generalized norms and moments. Triangular episodes are classified with the trend index and its derivative. The severity of the situations is evaluated by deviation indices. The trend episodes and the deviation indices provide good tools for detecting changes in water quality and for process control.

Data and Trend Analysis of Wastewater Treatment in Pulp and Paper Industry

Abstract Temporal reasoning is a very valuable tool to diagnose and control slow processes. For control system software it is a difficult problem to detect such patterns including trend extraction and similarity analysis. In this study, an intelligent trend index is developed from scaled measurements of a wastewater treatment plant. The scaling is based on monotonously increasing, nonlinear functions, which are generated with generalised norms and moments. The monotonous increase is ensured with constraint handling. Triangular episodes are classified with the trend index and the derivative of it. Severity of the situations is evaluated by a deviation index which takes into account the scaled values of the measurements. Modelling and simulation of biological wastewater treatment in pulp and paper industry requires hybrid models since the operating conditions can fluctuate drastically. A compact dynamic simulation is realized with linguistic equation (LE) models, which consist of two parts: interactions are handled with linear equations, and nonlinearities are taken into account by membership definitions. The same scaling approach is used in trend analysis and modelling. The LE based trend episodes and deviation indices provide good tools for detecting changes in operating conditions to be used in data and model selection and model adaptation.

Comparison of modelling accuracy with and without exploiting automated optical monitoring information in predicting the treated wastewater quality

ABSTRACT Traditionally the modelling in an activated sludge process has been based on solely the process measurements, but as the interest to optically monitor wastewater samples to characterize the floc morphology has increased, in the recent years the results of image analyses have been more frequently utilized to predict the characteristics of wastewater. This study shows that the traditional process measurements or the automated optical monitoring variables by themselves are not capable of developing the best predictive models for the treated wastewater quality in a full-scale wastewater treatment plant, but utilizing these variables together the optimal models, which show the level and changes in the treated wastewater quality, are achieved. By this early warning, process operation can be optimized to avoid environmental damages and economic losses. The study also shows that specific optical monitoring variables are important in modelling a certain quality parameter, regardless of the other input variables available.

Monitoring a municipal wastewater treatment process using a trend analysis

ABSTRACT New monitoring methods are required to enhance the operation of a wastewater treatment process and to meet the constantly tightening regulations for the effluent discharges. An on-line optical monitoring device, that analyses the morphological parameters of the flocs, has been shown to be a potential tool for assessing the wastewater quality and the state of the activated sludge process. In this paper, the earlier presented trend analysis method is applied to the operating conditions, the treatment results and the optical monitoring variables of a full-scale biological wastewater treatment process. The trend episodes and the deviation indices resulted from the trend analysis provide warning of the changes in the monitored variables and the received information can be used as assistance in the treatment process operation and avoiding harmful environmental risks.

Water Quality Modelling and Control in a Water Treatment Process

Drinking water quality is an important issue around the world since the low quality water causes health-related problems and economic losses. To ensure high quality water an efficient monitoring and control of a water treatment process is essential. In this study, two common quality variables of treated water, turbidity and residual aluminium, are modelled using the cross-validation method. Selected variables for developing the models are easy and reliable to measure on-line from raw water source. The linguistic equation (LE) approach based on nonlinear scaling and linear interactions produces models, which can be used in addition to predicting the water quality, for monitoring and controlling the water treatment process. The goal of the control simulation was to minimize the turbidity by controlling the coagulation chemical dose and see how this affects the residual aluminium level in drinking water. The results showed that the developed models were accurate and followed the changes in measured water quality variable. Results of the control simulation suggest that the water quality can be improved by proper control and optimizing the chemical dosing, as minimizing the turbidity reduces the residual aluminium level.