Modelling control strategies for chemical phosphorus removal at Tivoli wastewater treatment plant

• Main Author: Rosendahl, Sara
• Format: Others
• Language: English
• Published: Uppsala universitet, Institutionen för geovetenskaper 2021
• Subjects: Wastewater treatment plant; phosphorus precipitation; control strategy; feedforward; feedback; ASM2d and primary clarifier model.; Water Engineering; Vattenteknik
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-453546

Wastewater compose an environmental risk as it contains high levels of nutrients, including phosphorus. Wastewater treatment plants (WWTPs) reduce phosphorus by using coagulants that precipitate soluble phosphate into metal phosphate, which is separated by settling. Coagulant flow is regulated by a control strategy, typically feedforward or feedback control. Feedforward is based on incoming wastewater disturbances whereas feedback control uses outgoing process values. Incoming phosphate is hard to measure and can be estimated using soft sensors. Modelling control strategies can help decide which strategy that is most suitable. Models describing phosphorus removal are Activated Sludge Model, ASM2d, and primary clarifier model. ASM2d models phosphorus precipitation and the primary clarifier model settling of particles. Tivoli WWTP faces challenges to reach effluent requirements of phosphorus. The wastewater flows through an equalisation tank, Regnbågen, before being pumped to Tivoli. Particulate matter settles in Regnbågen, which is removed by reducing the water level in Regnbågen. This rapidly increases incoming particulate load to Tivoli.Tivoli’s current control strategy is feedforward proportional to suspended solids. It is suspected, that this strategy overdose coagulant during the emptying of Regnbågen. The purpose of this thesis was to find the optimal control strategy for phosphorus precipitation at Tivoli WWTP, by using a model-based approach. Control strategies modelled are; feedforward, feedback and these two control strategies combined. Additional issues resolved are 1) calibration of a model that predicts the effect of chemical dosage on effluent phosphorus concentration from the primary clarifier, 2) calibrationof a soft sensor, 3) determination of which control strategy that is most suitable. ASM2d and a primary clarifier model were used to create a model describing chemical phosphorus removal. The calibration matches measured phosphate concentration, but underestimate peaks. The primary clarifier model was calibrated by minimising load differences for phosphate and total suspended solids, and was calibrated satisfyingly. A simplified soft sensor was constructed, described by a linear relationship between phosphate and pH. Three disturbances for feedforward control were analysed; measured phosphate, the soft sensors estimation of phosphate and Tivoli’s current controlstrategy. The optimal control strategy was found through a multi-criteria analysis. The optimal control strategy is the combined control strategy, when feedforward is proportional to incoming measured phosphate. The performance of all analysed feedforward disturbances were significantly improved when combined with feedback control. Furthermore, consequential errors are distinct when the soft sensor miss-predictincoming phosphate concentration. If the phosphate concentration cannot be correctly measured/estimated, feedback control alone has the best performance.