Model Predictive Control of Stochastic Wastewater Treatment Process for Smart Power, Cost-Effective Aeration

Stentoft, Peter Alexander; Guericke, Daniela; Munk-Nielsen, Thomas; Mikkelsen, Peter Steen; Madsen, Henrik; Vezzaro, Luca; Møller, Jan Kloppenborg

doi:10.1016/j.ifacol.2019.06.132

Cited by 10 publications

(17 citation statements)

References 6 publications

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“…A sketch of the AASP is shown in Figure 1. The key concept behind the AASP is that the anoxic and aerobic treatment alternates in controlled cycles to secure good treatment [26], allowing the entire nitrification/denitrification process to occur in a single tank. The aeration process is typically accomplished in large tanks, with compressors specifically sized to the hydro-static head and airflow required to facilitate aerobic treatment [18,20,27].…”

Section: The Aeration Processmentioning

confidence: 99%

“…The compressors have to deliver large amounts of air during the aerobic cycles, consequently, the aeration process is an energy demanding process responsible for up to 75% of the total electric power demand at a WWTP [27][28][29][30]. However, electricity consumption should not be considered solely, as the discharge of nutrients is object to taxation, e.g., 30 DKK/kg discharged in Denmark [26]. Therefore, cost-analysis suggests aeration should be minimized while ensuring a satisfactory extent of nitrification [26,31].…”

Section: The Aeration Processmentioning

confidence: 99%

See 1 more Smart Citation

Compressor Scheduling and Pressure Control for an Alternating Aeration Activated Sludge Process—A Simulation Study Validated on Plant Data

Hansen

Veng

Durdevic

2021

Water

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Aiming at reducing their emissions, wastewater treatment plants (WWTP) seek to reduce their energy consumption, where a large amount is used for the aeration. The case plant, Grindsted WWTP uses an alternating aeration strategy, with a common air supply system facilitating the process in four aeration tanks and thus making optimisation challenging. In this work, a nonlinear model of the air supply system is designed, in which multiple key parameters are estimated by data-driven optimization. Subsequently, a model-based control strategy for scheduling of compressors and desired airflow is proposed, to save energy without compromising the aeration performance. The strategy is based upon partly static- partly dynamic models of the compressors, describing their efficiency in terms of system head and volumetric airflow rate. The simulation study uses real plant data and shows great potential for improvement of energy efficiency, regardless of the aeration pattern in any of the four process tanks, and furthermore contributes to a reduction in compressor restarts per day. The proposed method is applicable to other WWTP with multiple compressors in the air supply system, as this study is conducted using first principle models validated on data from the daily operation.

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Section: The Aeration Processmentioning

confidence: 99%

Section: The Aeration Processmentioning

confidence: 99%

Compressor Scheduling and Pressure Control for an Alternating Aeration Activated Sludge Process—A Simulation Study Validated on Plant Data

Hansen

Veng

Durdevic

2021

Water

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“…Stentoft et al [58] developed a serial GB model for prioritizing aeration in economical schedule in wastewater treatment plants. The genetic algorithm (GA) was integrated with stochastic differential equations, based on the WB model of the process.…”

Section: Water Treatmentmentioning

confidence: 99%

“…"ANN and GA", "LS cost function", "EKF", "non-linear fitting model" [58][59][60] Water treatment estimation and control aeration tank "serial", "parallel", "combined" "ammonium and nitrate concentration", "dissolution rate" "EKF", "ANN" [61][62][63][64] Material processing and energy materials estimation and control "return materials authorization process", "thermotronic system", "photo-voltaic cell" serial temperature "fuzzy membership function", "SDO", "LS" Table 3. GB modeling application in industrial robots and miscellaneous cases.…”

Section: Paper Industrymentioning

confidence: 99%

“…The GB models integrate the WB and BB models for addressing the concerns, i.e., accuracy and intuitiveness, of industrial operators.Applications of GB models are found in a variety of domains in process industries such as design, estimation, control, and monitoring. They are applied across the process industries such as iron and steel making [32][33][34][35][36], food processing [37][38][39], oil and gas processing [40-44], chemical, biochemical, and pharmaceutical [45-54], power plants [55][56][57], water treatment [58][59][60], material processing and energy materials [61-64] and industrial robots [65][66][67].The aim of the current study is to do a comprehensive review of the fundamentals of GB soft sensors, their applications in the process industry and prospects in the era of big data analytics; to the best of the authors' knowledge, no extensive review that addresses all such dimensions is reported in the literature. GB soft sensors application in selected process industries were investigated.…”

mentioning

confidence: 99%

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Gray-box Soft Sensors in Process Industry: Current Practice, and Future Prospects in Era of Big Data

et al. 2020

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Virtual sensors, or soft sensors, have greatly contributed to the evolution of the sensing systems in industry. The soft sensors are process models having three fundamental categories, namely white-box (WB), black-box (BB) and gray-box (GB) models. WB models are based on process knowledge while the BB models are developed using data collected from the process. The GB models integrate the WB and BB models for addressing the concerns, i.e., accuracy and intuitiveness, of industrial operators. In this work, various design aspects of the GB models are discussed followed by their application in the process industry. In addition, the changes in the data-driven part of the GB models in the context of enormous amount of process data collected in Industry 4.0 are elaborated.

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Probabilistic Intraday Wastewater Treatment Plant Inflow Forecast Utilizing Rain Forecast Data and Sewer Network Sensor Data

Sonnenschein

Ziel

2022

Preprint

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Forecasting of wastewater treatment plant inflow dynamics constitutes an enabler technology for wastewater treatment process optimization using model predictive control. However, accurate inflow prediction is still challenging, especially for strong rainfall events, where complex system dynamics and missing information on future rainfall represent limiting factors. We propose a seasonal probabilistic time series model for modelling the short-term wastewater inflow accurately while providing quantification of forecast uncertainty. To ensure suitability for practical implementation, the unconstrained parameters of the predictive distribution are modelled as linear functions of the input variables in the framework of Generalized Additive Models for Location Scale and Shape. Non-linear effects are approximated by Rectified Linear Units (ReLU), accounting for buffering within the sewer network and flow-dependent catchment response time. In addition to water level measurements from within the sewer network and rain rate measurements, rain forecasts are incorporated as exogenous regressors, where historical rain forecasts are used for model calibration. The model performance is evaluated on historical data from a German wastewater treatment plant using deterministic and probabilistic scoring rules. We benchmark against probabilistic time series models (SARX) and LSTMs. Our results show that the proposed model unites the benefits of high prediction accuracy of the LSTM and enhanced intelligibility of the SARX model, but accurate real-time rain forecasts are mandatory for successful real-world implementation.

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Model Predictive Control of Stochastic Wastewater Treatment Process for Smart Power, Cost-Effective Aeration

Cited by 10 publications

References 6 publications

Compressor Scheduling and Pressure Control for an Alternating Aeration Activated Sludge Process—A Simulation Study Validated on Plant Data

Compressor Scheduling and Pressure Control for an Alternating Aeration Activated Sludge Process—A Simulation Study Validated on Plant Data

Gray-box Soft Sensors in Process Industry: Current Practice, and Future Prospects in Era of Big Data

Probabilistic Intraday Wastewater Treatment Plant Inflow Forecast Utilizing Rain Forecast Data and Sewer Network Sensor Data

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