Ignacio Santín

Model predictive control and fuzzy control in a hierarchical structure for wastewater treatment plants

This paper presents a hierarchical two-level control structure for a biological wastewater treatment plant (WWTP), with the goal of simultaneously improving the effluent quality and reducing operational costs. The Benchmark Simulation Model No.1 is used as working scenario. The idea is to adjust the dissolved oxygen in the fifth tank (DO5) according with the present working conditions, instead of keeping it in a fixed value. Thus is concreted by a hierarchical structure. MPC with inlet flow rate feedforward control (MPC + FF) is proposed for the lower level to control nitrate nitrogen concentration of the second tank (NO2) and DO5. The high level is the responsible for adjusting the DO setpoint in the fifth tank (DO5 setpoint) of the controller of the low level, according with the ammonium and ammonia nitrogen concentration in the fifth tank (NH5). The controller for the high level is based on a Fuzzy control. A tuning region has been determined for the high level controller, in which the results show a simultaneously improvement of the effluent quality and operational cost.

Control strategies for nitrous oxide emissions reduction on wastewater treatment plants operation

The present paper focused on reducing greenhouse gases emissions in wastewater treatment plants operation by application of suitable control strategies. Specifically, the objective is to reduce nitrous oxide emissions during the nitrification process. Incomplete nitrification in the aerobic tanks can lead to an accumulation of nitrite that triggers the nitrous oxide emissions. In order to avoid the peaks of nitrous oxide emissions, this paper proposes a cascade control configuration by manipulating the dissolved oxygen set-points in the aerobic tanks. This control strategy is combined with ammonia cascade control already applied in the literature. This is performed with the objective to take also into account effluent pollutants and operational costs. In addition, other greenhouse gases emissions sources are also evaluated. Results have been obtained by simulation, using a modified version of Benchmark Simulation Model no. 2, which takes into account greenhouse gases emissions. This is called Benchmark Simulation Model no. 2 Gas. The results show that the proposed control strategies are able to reduce by 29.86% of nitrous oxide emissions compared to the default control strategy, while maintaining a satisfactory trade-off between water quality and costs.

Control strategies for the sludge line in wastewater treatment plants

This paper presents the implementation and evaluation of control strategies for wastewater treatment plants. The study was carried out using the platform Benchmark Simulation Model No. 2 (BSM2) and is conceived plant wide by introducing as a distinctive feature the structuring of the plant on: a water line and a sludge line, the two operating lines being interdependent but at the same time influencing simultaneously the overall functioning of the plant. For the water line only one control strategy was implemented and this strategy was used together with the five control strategies proposed for the sludge line. In evaluating the obtained results special attention has been placed on the indicators related to the percentage of time when the pollutants in the effluent are violating the quality limits for total nitrogen and ammonium. However, the effect on the overall effluent quality and operation costs have also been considered by means of the usual aggregated cost indexes. The control strategy for the sludge line that offered the best results is the one that uses the control of the total suspended solids in the fifth tank by manipulating the wastage flow rate together with the control of the storage tank volume by manipulating its flow rate.

Fuzzy logic for plant-wide control of biological wastewater treatment process including greenhouse gas emissions

The application of control strategies is increasingly used in wastewater treatment plants with the aim of improving effluent quality and reducing operating costs. Due to concerns about the progressive growth of greenhouse gas emissions (GHG), these are also currently being evaluated in wastewater treatment plants. The present article proposes a fuzzy controller for plant-wide control of the biological wastewater treatment process. Its design is based on 14 inputs and 6 outputs in order to reduce GHG emissions, nutrient concentration in the effluent and operational costs. The article explains and shows the effect of each one of the inputs and outputs of the fuzzy controller, as well as the relationship between them. Benchmark Simulation Model no 2 Gas is used for testing the proposed control strategy. The results of simulation results show that the fuzzy controller is able to reduce GHG emissions while improving, at the same time, the common criteria of effluent quality and operational costs.

Optimization of the wastewater treatment processes based on the relaxation method

The use of control engineering in the case of wastewater treatment processes can be an efficient way to improve their performances. This paper deals with implementation and evaluation of an optimization procedure used to determine the optimal values of the setpoints and the fixed command variables from the low level of the control structure. The optimization procedure is based on the relaxation method, the minimum value for each coordinate being determined from a limited number of simulations that covers all the evolution range using the polynomial interpolation. The results are validated on the Benchmark Simulation Model No. 2 using the default control strategy implemented in this software package.

Tracking Improvement of the Basic Lower Level Loops

This chapter presents the lower level control of a hierarchical structure using BSM1 as working scenario. This lower level is based on the default control strategy, i.e., S\(_{O,5}\) and S\(_{NO,2}\) control by manipulating K\(_L\)a\(_5\) and Q\(_a\), respectively. Next, the S\(_{O}\) control is extended to the third and fourth tanks by manipulating K\(_L\)a\(_3\) and K\(_L\)a\(_4\). In this chapter, MPC\(+\)FF has been employed with the objective of improving the S\(_{O}\) and S\(_{NO,2}\) tracking in comparison with the default control strategy and with the literature.

Advanced Decision Control System

This chapter presents the control strategies applied in BSM2 for S\(_{N_{tot,e}}\) or S\(_{{ NH},e}\) violations removal. This chapter continues with special emphasis on dealing with the concentration limits of effluent pollutants, in order to accomplish the established legal requirements. In this case, the greater variability in the influent provided by BSM2 requires an advanced decision control system in order to select the control strategies to be applied based on the effluent predictions explained in the previous chapter.

Effluent Predictions for Violations Risk Detection

This chapter deals with the elimination of S\(_{N_{tot,e}}\) and S\(_{NH,e}\) using BSM2, which provides a more elaborated and variable influent with an assessment of one year. Applying control strategies to avoid effluent violations, only when an increase of contaminants is already detected in the reactors, is not enough in BSM2. Due to this fact, an effluent prediction of the pollutants based on some variables in the influent is required. ANNs are implemented with this aim.

Process Modelling and Simulation Scenarios

The wastewater research community has extensively applied benchmark models to develop and evaluate control strategies for WWTPs. The large number of journal papers and conferences related to the use of benchmarks in WWTPs, issued up to the present, prove the utility of these tools. This book also makes use of benchmark models to test and compare the proposed control strategies. These benchmarks are briefly described in the first section of this chapter.

Denitrification and Nitrification Processes Improvement for Avoiding Pollutants Limits Violations in the Effluent

In this chapter, different control strategies are applied with the aim of avoiding S\(_{N_{tot,e}}\) or S\(_{NH,e}\) violations using BSM1 as testing plant. These control strategies are implemented simultaneously with the hierarchical control structure explained in previous chapter, in order to achieve, at the same time, an EQI and OCI reduction. The tuning of both higher level controllers is modified based on the required objective.