Benchmark Simulation Model Research Articles

Data-Driven Tube-Based Robust Predictive Control for Constrained Wastewater Treatment Process.

The wastewater treatment process (WWTP) is characterized by unknown nonlinearity and external disturbances, which complicates the tracking control of dissolved oxygen concentration (DOC) within operational constraints. To address this issue, a data-driven tube-based robust predictive control (DTRPC) strategy is proposed to achieve stable tracking control of DOC and satisfy the system constraints. First, a tube-based robust predictive control (TRPC) strategy is designed to deal with system constraints and external disturbances. Specifically, a nominal controller is designed to ensure that the nominal output accurately tracks the set-point under tightened constraints, while an auxiliary feedback controller is designed to suppress disturbances and restore the nominal performance of the disturbed WWTP. Second, two fuzzy neural network identifiers are employed to provide accurate predictive outputs for the control process, overcoming the challenges of modeling the WWTP with strong nonlinearity and unknown dynamics. Third, the generalized multiplier method is utilized to solve the constrained optimization problem to obtain the nominal control law, and the gradient descent algorithm is used to obtain the auxiliary control law. The implementation of this composite controller ensures the satisfaction of the system constraints and the effective suppression of disturbances. Finally, the feasibility and stability of the proposed DTRPC strategy are guaranteed through rigorous theoretical analysis, and its effectiveness is demonstrated through the simulations on the benchmark simulation model No.1.

Optimization control of wastewater treatment based on neural network and multi-objective optimization algorithm

In wastewater treatment, energy consumption and effluent quality are conflicting objectives. Improving effluent quality while reducing energy consumption has become a pressing issue. This paper proposes a dynamic optimal control method for the wastewater treatment process, utilizing neural networks and multi-objective dragonfly algorithm. A regression prediction model, based on a Self-Attention mechanism of a multi-scale convolutional neural network-bidirectional gated recurrent unit, is used to establish a soft measurement model for effluent quality and energy consumption, serving as the optimization objective function. The multi-objective dragonfly algorithm then optimizes this objective to determine the optimal set values for control variables. A Linear Active Disturbance Rejection Control, based on a scalable bandwidth linear extended state observer, is designed to track these optimal set values. The proposed method is tested using Benchmark Simulation Model No. 1 (BSM1). Results demonstrate that the method effectively obtains and tracks optimal set values of control variables, ensuring effluent quality meets standards and significantly reducing energy consumption compared to four other methods.

Neural Network-Based Adaptive Tracking Control for Denitrification and Aeration Processes With Time Delays.

Wastewater treatment process (WWTP), consisting of a class of physical, chemical, and biological phenomena, is an important means to reduce environmental pollution and improve recycling efficiency of water resources. Considering characteristics of the complexities, uncertainties, nonlinearities, and multitime delays in WWTPs, an adaptive neural controller is presented to achieve the satisfying control performance for WWTPs. With the advantages of radial basis function neural networks (RBF NNs), the unknown dynamics in WWTPs are identified. Based on the mechanistic analysis, the time-varying delayed models of the denitrification and aeration processes are established. Based on the established delayed models, the Lyapunov-Krasovskii functional (LKF) is used to compensate for the time-varying delays caused by the push-flow and recycle flow phenomenon. The barrier Lyapunov function (BLF) is used to ensure that the dissolved oxygen (DO) and nitrate concentrations are always kept within the specified ranges though the time-varying delays and disturbances exist. Using Lyapunov theorem, the stability of the closed-loop system is proven. Finally, the proposed control method is carried out on the benchmark simulation model 1 (BSM1) to verify the effectiveness and practicability.

Unified control of diverse actions in a wastewater treatment activated sludge system using reinforcement learning for multi-objective optimization

The operation of modern wastewater treatment facilities is a balancing act in which a multitude of variables are controlled to achieve a wide range of objectives, many of which are conflicting. This is especially true within secondary activated sludge systems, where significant research and industry effort has been devoted to advance control optimization strategies, both domain-driven and data-driven. Among data-driven control strategies, reinforcement learning (RL) stands out for its ability to achieve better than human performance in complex environments. While RL has been applied to activated sludge process optimization in existing literature, these applications are typically limited in scope, and never for the control of more than three actions. Expanding the scope of RL control has the potential to increase the optimization potential while concurrently reducing the number of control systems that must be tuned and maintained by operations staff. This study examined several facets of the implementation of multi-action, multi-objective RL agents, namely how many actions a single agent could successfully control and what extent of environment data was necessary to train such agents. This study observed improved control optimization with increasing action scope, though control of waste activated sludge remains a challenge. Furthermore, agents were able to maintain a high level of performance under decreased observation scope, up to a point. When compared to baseline control of the Benchmark Simulation Model No. 1 (BSM1), an RL agent controlling seven individual actions improved the average BSM1 performance metric by 8.3 %, equivalent to an annual cost savings of $40,200 after accounting for the cost of additional sensors.

Towards stable and efficient nitrogen removal in wastewater treatment processes via an adaptive neural network based sliding mode controller

Advanced controllers often offer an innovative solution to proper quality control in wastewater treatment processes (WWTPs). However, nonlinearity and uncertain disturbances usually make the conventional control strategies inadequate or impossible for the stable operations of WWTPs. To guarantee the stability of ammonia nitrogen concentration (SNH) control in WWTPs, a direct adaptive neural networks-based sliding mode control (ANNSMC) strategy has been proposed in this article. A sliding mode controller is designed and implemented with the help of an adaptive Neural Network (ANN), named Radial Basis Function Neural Network (RBFNN), which can approach the desired control law accurately. Also, the stability of a system installed with the ANNSMC is analyzed by using the Lyapunov theorem, which ensures system robustness and adaptability. Additionally, to deal with high energy consumption and low treatment efficiency problems in the wastewater denitrification processes, this paper proposes a dual-loop denitrification control strategy and validates it in the Benchmark Simulation Model No.2 (BSM2) platform. The strategy can strengthen the denitrification efficiency by collaborating the SNH with nitrate nitrogen (SNO) concentration in the WWTPs properly. The experimental results demonstrate that the proposed strategy can obtain remarkable stability and robustness, reducing energy consumption effectively compared with other standard and advanced control strategies.

Multiobjective Optimization of Papermaking Wastewater Treatment Processes under Economic, Energy, and Environmental Goals.

Due to the heterogeneity of recycled paper materials and the production conditions, pollutants in papermaking wastewater fluctuate sharply over time. Quality control of the papermaking wastewater treatment process (PWTP) is challenging and costly. As regulations are also growing about the environmental effects of the PWTP on greenhouse gas (GHG) emission, energy consumption, etc., the PWTP formulates a complex multiobjective optimization problem. This research established a multiagent deep reinforcement learning framework to simultaneously optimize process cost, energy consumption, and GHG emission in the PWTP, subjected to the effluent quality, to realize economic, energy, and environmental (3E) goals. The biological treatment process of wastewater in paper mills was simulated using benchmark simulation model no. 1 (BSM1). The data generated based on the BSM manual was utilized for model training, and real data acquired from a local papermaking factory was used to estimate the model performance. The results show that the proposed method outperforms conventional techniques in identifying the best control strategies for multiple targets.

Interpretable GHG emission prediction for papermaking wastewater treatment process with deep learning

The content of pollutants in papermaking wastewater is highly varied due to heterogeneous raw materials, product types, and biochemical process conditions, resulting in significant uncertainty of GHG emissions mitigation in the treatment process. This study established a modified Benchmark Simulation Model no.1-based mechanism model to reveal the characteristics of GHG emissions and support GHG reduction in the papermaking wastewater treatment process. Due to its heavy dependence on the Benchmark Simulation Model No. 1 (BSM1) and plentiful parameters, it was instead further proposed Deep Neural Networks (DNN) models and Long Short-Term Memory (LSTM) models to estimate GHG emissions with high accuracy (R2 > 0.94) greatly reduced computational complexity (8920.6 times). The global sensitivity analysis upon DNN revealed key factors for GHG emission with strong explainability, supporting the development of GHG reduction control strategies for the papermaking wastewater treatment process.

Twinning quality sensors in wastewater treatment process via optimized echo state network-based soft sensors

The presence of a large amount of quality-related but hard-to-measure variables usually makes effective monitoring of industrial processes difficult, and even impossible. Soft computing techniques and digital twins can revolutionize standard approaches to solve this issue, even though industrial data are frustrated by nonlinearity, disturbances, and dynamic behaviors. In this light, this paper proposes a data-driven soft sensor with the help of the Fourier amplitude sensitivity test (FAST) and improved probabilistic regularized echo state network (IPRESN), then to twin the quality hardware sensors. Within this framework, the initial step is to rely on the FAST method to measure the collaborated importance of multiple process variables with respect to each quality variable. This quantification facilitates the selection of auxiliary variables for the training of IPRESN. Furthermore, a probabilistic regularization method is learned to improve the robust performance of the echo state network (ESN) by re-designing a new objective function to minimize the mean and variance of the modeling error. Additionally, the whale optimization algorithm (WOA) is improved and then used to optimize the critical hyper-parameters of ESN, thus mitigating the risks associated with suboptimal hyperparameter selection. The effectiveness of the proposed method is verified through a Benchmark Simulation Model 2 (BSM2) and a full-scale wastewater treatment plant data set. The findings show the potential of the proposed method in facilitating digital twin implementations for hardware sensors.

Adaptive type-2 fuzzy-neural switching control for wastewater treatment process under several operating conditions

The effluent ammonia and total nitrogen variation lead to multiple operating conditions in wastewater treatment process (WWTP), which challenges the control strategies based on a single control model. Therefore, it is essential to design suitable control strategies for WWTP to enhance the control performance under different operating conditions. In this paper, an adaptive type-2 fuzzy-neural switching control (AT2FSC) strategy is proposed to solve this problem. First, a soft-sensing model is developed based on fuzzy neural network and influent data in WWTP to estimate the effluent ammonia nitrogen and total nitrogen status. Then, the online operating conditions are determined by the critical values of effluent ammonia nitrogen and total nitrogen. Second, the corresponding fuzzy rules are devised for different operating conditions and a type 2 fuzzy switching model (AT2FSM) is constructed to capture the operating status changes of WWTP. Then, the proposed AT2FSC can design the switching control strategy according to AT2FSM to adapt to the current operating condition. Third, the Lyapunov theorem ensures the stability of the proposed AT2FSC method. Then, it facilitates the further application of AT2FSC in WWTP. Finally, the performance of AT2FSC under various operating conditions is validated on the benchmark simulation model No.1 (BSM1). Simulation results under four different operating conditions demonstrate that the proposed AT2FSC strategy can reduce the excess peak of effluent ammonia and total nitrogen while maintaining the operating performance.

Exploring the effects of faults on the performance of a biological wastewater treatment process

ABSTRACT To prioritise which faults should be detected in a biological wastewater treatment process, and with what level of urgency, it is necessary to understand the effect that they have on the process. Using the Benchmark Simulation Model No. 1 and 2. (BSM1 and BSM2), several process and sensor faults were considered and their impacts on various cost, quality, and controller performance evaluation metrics analysed. Both the cost of treating the wastewater and the quality of the effluent were impacted in varying degrees of severity by the faults tested. The most influential faults in both models were decreases to autotrophic and heterotrophic growth rates, decreases to the heterotrophic death rate, and the inhabitation fault. It was shown that only larger fault sizes were significant, and the required speed of detection is dependent on the fault profile. Prioritising detection of the most influential faults was shown to have significant effects on monitoring requirements for fault detection and the subsequent complexity required of a fault detection system. A valuable takeaway was the similarity of results from BSM1 and BSM2; the consistency of the influential process faults suggests that systems that can be described by these models are likely affected by the same faults.

Fault Detection of Urban Wastewater Treatment Process Based on Combination of Deep Information and Transformer Network.

As one of the hot issues of concerns during modern social development, the wastewater treatment process is acknowledged to be a process with complex biochemical reactions and susceptible to an external environment, featuring strong nonlinear and time correlation characteristics, which are difficult for traditional mechanism-based models to tackle. For many classical data-driven fault detection methods, a complete retraining process is necessary to monitor every new fault, and most of the current neural network-based strategies rarely achieve satisfactory monitoring accuracy or robustness either. Giving full consideration to the aforementioned problems, this article takes advantage of position encoding, residual connection, and multihead attention mechanism embedded in the Transformer structure to establish an effective and efficient wastewater treatment process fault detection model, where offline modeling and online monitoring are performed successively to achieve accurate detection of the faults. In the experimental part, the advantages of the proposed method are strongly verified through the simulation monitoring of 27 faults on the benchmark simulation model 1 (BSM1), where the false alarm rate (FAR) and miss alarm rate (MAR) of the established method are proved to be significantly lower than those of the compared state-of-the-art methods.

Optimizing wastewater treatment plant operational efficiency through integrating machine learning predictive models and advanced control strategies

This research optimizes wastewater treatment plant (WWTP) operational performance by integrating advanced control strategies and predictive modeling. Emphasizing the significance of machine learning (ML), feature extraction techniques (filter, wrapper, and embedded methods) were employed to develop robust prediction models. The random forest (RF) model was applied to predict target variables, effluent ammonia, and nitrogen concentrations. Integrating these predictive models into the WWTP’s control system is necessary for enhanced efficiency and pollution regulation. Benchmark Simulation Model 1 (BSM1) was used as the WWTP model. The two tested control strategies included a hybrid approach, combining feedforward and feedback control, resulting in an improved effluent quality index (EQI), a marginal increase in aeration energy (AE) and the operational cost index (OCI), and a significant decrease in effluent ammonia concentration. The second strategy utilized self-organizing fuzzy inference system (SOFIS) control, resulting in promising outcomes with improvements in EQI, ammonia, and nitrogen concentrations, with negligible increases in AE and OCI. The findings highlight the pivotal role of predicting effluent quality parameters and integrating the prediction into WWTP control systems. This integrated approach proves effective in optimizing pollutant regulation and overall system performance. The research provides insights into the practical implementation of ML-based control strategies in wastewater treatment. It offers future scope for exploring advanced ML algorithms and their real-time application in operational WWTPs. This research introduces a novel approach by integrating machine learning with the BSM1 weather dataset and sensor data for feature selection to predict effluent concentrations in a WWTP. Through the comparative analysis with the default proportional-integral (PI) control configuration, the research highlights the importance of integrating machine learning techniques into WWTP control systems.

Towards good modelling practice for parallel hybrid models for wastewater treatment processes.

This study explores various approaches to formulating a parallel hybrid model (HM) for Water and Resource Recovery Facilities (WRRFs) merging a mechanistic and a data-driven model. In the study, the HM is constructed by training a neural network (NN) on the residual of the mechanistic model for effluent nitrate. In an initial experiment using the Benchmark Simulation Model no. 1, a parallel HM effectively addressed limitations in the mechanistic model's representation of autotrophic bacteria growth and the data-driven model's incapability to extrapolate. Next, different versions of a parallel HM of a large pilot-scale WRRF are constructed, using different calibration/training datasets and different versions of the mechanistic model to investigate the balance between the calibration effort for the mechanistic model and the compensation by the NN component. The HM can improve predictions compared to the mechanistic model. Training the NN on an independent validation dataset produced better results than on the calibration dataset. Interestingly, the best performance is achieved for the HM based on a mechanistic model using default (uncalibrated) parameters. Both long short-term memory (LSTM) and convolutional neural network (CNN) are tested as data-driven components, with a CNN HM (root-mean-squared error (RMSE) = 1.58 mg NO3-N/L) outperforming an LSTM HM (RMSE = 4.17 mg NO3-N/L).

Making waves: Power-to-X for the Water Resource Recovery Facilities of the future

The wastewater industry and the energy system are undergoing significant transformations to address climate change and environmental pollution. Green hydrogen, which will be mainly obtained from renewable electricity water electrolysis (Power-to-Hydrogen, PtH), has been considered as an essential energy carrier to neutralize the fluctuations of renewable energy sources. PtH, or Power-to-X (PtX), has been allocated to multiple sectors, including industry, transport and power generation. However, considering its large potential for implementation in the wastewater sector, represented by Water Resource Recovery Facilities (WRRFs), the PtX concept has been largely overlooked in terms of planning and policymaking. This paper proposes a concept to implement PtX at WRRFs, where sourcing of water, utilization of the oxygen by-product, and PtX itself can be sustainable and diversified strategies. Potential value chains of PtX are presented and illustrated in the frame of a WWRF benchmark simulation model, highlighting the applications of oxygen from PtX through pure oxygen aeration and ozone disinfection. Opportunities and challenges are highlighted briefly, and so is the prospective outlook to the future. Ultimately, it is concluded that ‘coupling PtX to WRRFs’ is a promising solution, which will potentially bring sustainable opportunities for both WRRFs and the energy system. Apart from regulatory and economic challenges, the limitations in coupling PtX to WRRFs mainly come from energy efficiency concerns and the complexity of the integration of the water framework and the energy system.

Plant-Wide Models for Optimizing the Operation and Maintenance of BTEX-Contaminated Wastewater Treatment and Reuse

Benzene, toluene, ethylbenzene and xylenes, collectively known as BTEX compounds, are significant emerging contaminants in municipal wastewater. Stricter effluent quality regulations necessitate their removal, especially with concerns about organic micropollutant concentrations. Water scarcity further underscores the need for wastewater treatment to ensure safe agricultural or drinking water supplies. Although biological treatment partially reduces BTEX levels through processes like biodegradation and sorption, additional purification using physico-chemical methods is crucial for substantial reduction. This paper aims to outline plant-wide simulation methods for treating BTEX-contaminated sewage and facilitating reuse, adhering to IWA Good Modelling Practice Guidelines. The model, built upon the MiniSumo process model, incorporates equations detailing BTEX metabolism and removal kinetics, informed by an extensive literature review. Using a variant of the Benchmark Simulation Model with granular activated carbon for water reuse, the study examines strategies for improving effluent quality and minimizing operational costs. These strategies include adjusting the sludge retention time and airflow to enhance BTEX degradation and stripping, respectively, and comparing maintenance approaches for the GAC tower.

Energy efficiency in activated sludge process using adaptive iterative learning control with PI ABAC

This paper proposed an iterative learning control (ILC) with a feedback regulator based on proportional integral ammonium-based aeration control (PI ABAC) to improve dissolved oxygen control through data learning of iteration data. The proposed controller's performance is evaluated using benchmark simulation model no. 1. (BSM1). The assessments focused on four main areas: effluent violation, effluent quality, aeration energy, and overall cost index. The proposed ILC PI ABAC controller's effectiveness is evaluated by comparing the performance of the activated sludge process to the BSM1 PI and feedback PI ABAC under three different weather conditions: dry, rain, and storm. The improvement of the proposed method over BSM1 PI is demonstrated by a reduction in aeration energy of up to 24%. In conclusion, if the proposed ILC PI ABAC controller is given enough information, it can be quite successful in achieving energy efficiency.

Modeling key intermediates during anaerobic digestion of lipid rich kitchen waste with an extended ADM1.

Quantitative dynamics of the key intermediates, gases and carbohydrates during anaerobic digestion of different lipid rich kitchen waste and lipid rich model kitchen waste were modeled. Six batch reactors loaded with 25g l ( 39 l ) kitchen waste and model kitchen waste during a batch experiment were considered in simulation. Observed dynamics of carbohydrates, volatile organic acids and gases were described by an extended benchmark simulation model no. 2 (BSM2). In this study the extended BSM2 included a more detailed -oxidation for prediction of caproic acid. Furthermore, the extensions included carbohydrate digestion with an additional intermediate before propionic acid was released. In addition, a novel simplification approach for initial pH estimation was successfully applied. For parameter estimation a Markov Chain Monte Carlo method was used to obtain parameter distributions. With the presented model it was possible even with no calibrated data to predict point of times of intermediates maxima and propionic acid with relative stable concentration over several days for kitchen waste.

Enhancement of energy and cost efficiency in wastewater treatment plants using hybrid bio-inspired machine learning control techniques

Global population growth drives demand for clean water, energy, and wastewater production in urban areas. Most existing wastewater treatment plants (WWTPs) are energy- and cost-intensive. To achieve the UN Sustainable Development Goals (SDGs) by 2030, many regions have implemented strict policies for energy consumption and contaminated water discharge, promoting environmental sustainability. This paper attempts to enhance the achievement of SDGs in the wastewater treatment industry by proposing a novel method for hierarchical control of wastewater treatment plants to ensure energy and cost efficiency with less knowledge of the process model, fewer measurements, and fewer control loops. This controller is the combination of a machine learning control technique with a bio-inspired computational algorithm. To evaluate and validate the performance of the proposed controller on the WWTP, the benchmark simulation model no. 1 (BSM1) has been used. The most significant contributions of this paper show that 488.96 KWh/d, 3620.08 Kg/d, and 3454.55 KWh/d of aeration energy cost index, sludge production cost index, and operational cost index are respectively saved when compared to the default control strategy (DCS). Also, 893.69 Kg pollution units per day of effluent fine-related costs have been successfully recovered when compared to the DCS. The experimental results illustrate that the proposed control yields significant amelioration of the energy and cost profile of the WWTP with no effluent violation in dry, rain, and storm weather conditions.

Modeling greenhouse gas emissions from biological wastewater treatment process with experimental verification: A case study of paper mill

Wastewater treatment plants (WWTPs) have been regarded as the main sources of greenhouse gas (GHG) emissions. This study compares the influent characteristics of industrial wastewater represented by the WWTP of paper mill and that of domestic sewage represented by the Benchmark Simulation Model No. 1 (BSM1) under stormy weather. The various sources of GHG emissions from the two processes are calculated, and the contribution of each source to the total GHG emissions is assessed. Firstly, based on the mass balance analysis and the recognized emission factors, a GHG emission calculation model was established for the on-site and off-site GHG emission sources from the WWTP of paper mill. Simultaneously, a GHG emission experimental model was established by determining the dissolved concentrations of carbon dioxide (CO2) and nitrous oxide (N2O) in the papermaking wastewater, to verify the accuracy of the developed GHG calculation model. Subsequently, an optimum aeration rate for the paper mill was investigated to comply with the discharging norms. Under the optimum aeration rate of 10 h−1, the obtained calculation accuracies of CO2 and N2O emissions were 94.6 % and 91.1 %, respectively. The mean total GHG emission in the WWTP of paper mill was 550 kg CO2-eq·h−1, of which 44.6 % came from the on-site emission sources and 55.4 % from the off-site emission sources. It was also uncovered that the electrical consumption for aeration was the largest contributor to the total GHG emissions with a proportion of 25.2 %, revealing that the control strategy of the aeration rate is highly significant in reducing GHG emissions in WWTP of paper mills.

Integrated supervisory fuzzy control framework for biological wastewater treatment plants operation and their effect on GHG emissions

In this research, the effect on the greenhouse gases (GHG) emissions from biological wastewater treatment plants (WWTP) operation by implementing an integrated supervisory control framework on the bench-mark simulation model 2 gas (BSM2G) platform is studied. Ammonia based aeration control is considered for improving the efficiency of the nitrification process and also nitrate control is considered to enhance the performance of the overall plant. Different control strategies are proposed by considering PI control, Model Predictive control and Fuzzy controllers. A notable change is observed in GHG emissions due to bio-treatment because of the impact of control strategies on N2O emissions during denitrification with respect to the nitration where dissolved oxygen (DO) control alone is considered. In addition, when the nitrate loop is considered, a better change is observed in the overall plant performance. On comparison with other approaches, the proposed control strategies resulted in a reduction in the GHG emissions with marginal effect on the other performance indexes effluent quality index (EQI) and operational cost index (OCI).