Assessing wastewater reclamation potential by neural network model

A major change in paradigm is taking place in the operation of wastewater treatment plants as automatic process control is becoming feasible. This change is due to a number of different reasons, not least the development of online nutrient sensors, which measure the key parameters in the biological nutrient removal processes, i.e. ammonium, nitrate and phosphate. The thesis is about realising full-scale control in wastewater treatment systems using in situ nutrient sensors. The main conclusion of the work is that it is possible to significantly improve the operational performance in full-scale plants by means of relatively simple control structures and controllers based on in situ nutrient sensors. The in situ location should be emphasised as this results in short dead time, hence making simple feedback loops based on proportional and integral actions effective means to control the processes. This conclusion has been reached based on full-scale experiments, where various controllers and control structures for the biological removal of nitrogen and the chemical removal of phosphorous have been tested. The full-scale experiments have shown that it is possible to provide significant savings in energy consumption and precipitation chemicals consumption, reduction in sludge production and improvement of the effluent water quality. The conclusions are supported by model simulations using the COST benchmark simulation platform. The simulations are used for investigating issues regarding the interactions between the main control handles working in the medium time frame (relative gain array analysis). The simulations have also been used for testing various control structures and controllers. Controllers for the following types of control are suggested and tested: „h Control of aeration to obtain a certain effluent ammonium concentration; „h Control of internal recirculation flow rate to obtain maximum inorganic nitrogen removal; „h Control of external carbon dosage together with internal recirculation flow rate to obtain a certain effluent total inorganic nitrogen concentration; „h Optimisation of the choice of sludge age. Additionally, a procedure for implementing new control structures based on nutrient sensor has been proposed. The procedure involves an initial analysis phase, a monitoring phase, an experimenting phase and an automatic process control phase. An international survey with the aim to investigate the correspondence between ICA (instrumentation, control and automation) utilisation and plant performance has been carried out. The survey also gives insight into the current state of ICA applications at wastewater treatment plants. (Less)

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Realising full-scale control in wastewater treatment systems using in situ nutrient sensors

Control of nitrate recirculation flow in predenitrification systems

This paper presents the development and implementation of a Real-Time Expert System (RTES) for the supervision and control of a wastewater treatment pilot plant (WWTPP) with biological removal of organic matter and nutrients. The hardware architecture contains different supervision levels, including two autonomous process computers (plant control and analysers control) and a PLC, being the ES the top supervisory level. The RTES has been developed using a commercial, industrially validated RTES-development software G2™ (by Gensym Corp). It actuates as the master in a supervisory setpoint control (SSC) scheme and it is based on a distributed architecture. The knowledge is organised in several agents, representing the available knowledge for every sub-process of the WWTPP. In addition to these independent agents, a supervisor agent acts as the master of the multi-agent distributed system.

An expert supervisory system for a pilot WWTP

Improving the nitrogen removal efficiency of an A2/O based WWTP by using an on-line knowledge based expert system.

Nutrient sensor based real-time on-line process control of a wastewater treatment plant using recirculation

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Nutrient sensor based real-time on-line process control of a wastewater treatment plant using recirculation