Dissolved Oxygen Control in Biological Wastewater Treatments with Non-Ideal Sensors and Actuators

Fractional-order models identification and control within a supervisory control framework for efficient nutrients removal in biological wastewater treatment plants

Wastewater treatment plants (WWTPs) are highly non-linear processes that must be optimized to meet rigorous environmental water regulations. In this context, efficiency and costs are equally important terms. The ASM3bioP framework is employed in this study to enable simultaneous nitrogen and phosphorus removal using an activated sludge process model with seven-reactor configurations. The activated sludge process is the most complicated and energy-intensive phase of a WWTP. To control dissolved oxygen in aerobic reactors and nitrate levels in anoxic reactors, two robust PI controllers - a classical PI and a non-integer (fractional)-order PI - with both integer-order and fractional-order models are designed. The controllers are created and simulated with the use of a mathematical model that has been developed based on the input data. The lower level fractional controller with a fractional-order model improves both the effluent quality (EQI) and operational cost (OCI) indices significantly. For such biological WWTP, a hierarchical fuzzy logic controller is designed to adjust the dissolved oxygen in the seventh reactor (DO₇) to control ammonia. The implemented supervisory layer control strategy improves effluent quality EQI while increasing OCI marginally.

New approach for regulation of the internal recirculation flow rate by fuzzy logic in biological wastewater treatments

The internal recirculation plays an important role on the different biological processes of wastewater treatment plants because it has a great influence on the concentration of pollutants, especially nutrients. Usually, the internal recirculation flow rate is kept fixed or manipulated by control techniques to maintain a fixed nitrate set-point in the last anoxic tank. This work proposes a new control strategy to manipulate the internal recirculation flow rate by applying a fuzzy controller. The proposed controller takes into account the effects of the internal recirculation flow rate on the inlet of the biological treatment and on the denitrification and nitrification processes with the aim of reducing violations of legally established limits of nitrogen and ammonia and also reducing operational costs. The proposed fuzzy controller is tested by simulation with the internationally known benchmark simulation model no. 2. The objective is to apply the proposed fuzzy controller in any control strategy, only replacing the manipulation of the internal recirculation flow rate, to improve the plant operation.Therefore, it has been implemented in five operation strategies from the literature, replacing their original internal recirculation flow rate control, and simulation results are compared with those of the original strategies. Results show improvements with the application of the proposed fuzzy controller of between 2.25 and 57.94% in reduction of total nitrogen limit violations, between 55.22 and 79.69% in reduction of ammonia limit violations and between 0.84 and 38.06% in cost reduction of pumping energy.

Integrated data-driven strategy to optimize the processes configuration for full-scale wastewater treatment plant predesign

Wastewater treatment plants (WWTPs) play an irreplaceable role in eliminating pollutants from domestic and industrial wastewater and contribute to water recycling. Nowadays, the selection of processes configuration of WWTPs mainly depends on the local wastewater treatment standards and the experience of wastewater engineers rather than an intelligent data-driven strategy. In this study, an integrated data-driven strategy consisting of t-distributed stochastic neighbor embedding (t-SNE) and deep neural networks (DNNs) is proposed for optimizing the processes configuration of full-scale WWTP predesign. A large dataset with 14,647 samples collected from 10 full-scale WWTPs with distinct treatment processes is clustered by the t-SNE method based on the influent characteristics, and four meaningful clusters (Clusters I-IV) are identified for the subsequent development of DNN classification models. All four DNN models achieve acceptable classification accuracy (>0.8975) and the maximal testing accuracy is 0.9505. The DNN models are capable of finding the optimized processes configuration of WWTPs under target scenarios. Our results highlight the strength of combining the t-SNE and the DNN models to utilize the relationships between key parameters and processes configuration of WWTPs, and help engineers predesign WWTPs with the optimal processes configuration.

Industrial Control under Non-Ideal Measurements: Data-Based Signal Processing as an Alternative to Controller Retuning

Industrial environments are characterised by the non-lineal and highly complex processes they perform. Different control strategies are considered to assure that these processes are correctly performed. Nevertheless, these strategies are sensible to noise-corrupted and delayed measurements. For that reason, denoising techniques and delay correction methodologies should be considered but, most of these techniques require a complex design and optimisation process as a function of the scenario where they are applied. To alleviate this, a complete data-based approach devoted to denoising and correcting the delay of measurements is proposed here with a two-fold objective: simplify the solution design process and achieve its decoupling from the considered control strategy as well as from the scenario. Here it corresponds to a Wastewater Treatment Plant (WWTP). However, the proposed solution can be adopted at any industrial environment since neither an optimization nor a design focused on the scenario is required, only pairs of input and output data. Results show that a minimum Root Mean Squared Error (RMSE) improvement of a 63.87% is achieved when the new proposed data-based denoising approach is considered. In addition, the whole system performance show that similar and even better results are obtained when compared to scenario-optimised methodologies.