scholarly journals Machine Learning Weather Soft-Sensor for Advanced Control of Wastewater Treatment Plants

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3139 ◽  
Author(s):  
Félix Hernández-del-Olmo ◽  
Elena Gaudioso ◽  
Natividad Duro ◽  
Raquel Dormido
Keyword(s):  
Machine Learning ◽  
Wastewater Treatment ◽  
Control Systems ◽  
Weather Forecasting ◽  
Wastewater Treatment Plants ◽  
Weather Prediction ◽  
Weather Conditions ◽  
Soft Sensor ◽  
Physical Sensors ◽  
Useful Signal

Control of wastewater treatment plants (WWTPs) is challenging not only because of their high nonlinearity but also because of important external perturbations. One the most relevant of these perturbations is weather. In fact, different weather conditions imply different inflow rates and substance (e.g., N-ammonia, which is among the most important) concentrations. Therefore, weather has traditionally been an important signal that operators take into account to tune WWTP control systems. This signal cannot be directly measured with traditional physical sensors. Nevertheless, machine learning-based soft-sensors can be used to predict non-observable measures by means of available data. In this paper, we present novel research about a new soft-sensor that predicts the current weather signal. This weather prediction differs from traditional weather forecasting since this soft-sensor predicts the weather conditions as an operator does when controling the WWTP. This prediction uses a model based on past WWTP influent states measured by only a few physical and widely applied sensors. The results are encouraging, as we obtained a good accuracy level for a relevant and very useful signal when applied to advanced WWTP control systems.

scholarly journals Weather Prediction using Machine Learning and IOT

2020 ◽  
Vol 9 (4) ◽  
pp. 2094-2098
Keyword(s):  
Machine Learning ◽  
Weather Forecasting ◽  
Learning Algorithm ◽  
Weather Prediction ◽  
Weather Conditions ◽  
Data Sets ◽  
Machine Learning Algorithm ◽  
Time Data ◽  
Weather Parameters ◽  
Set Up

This project proposes a method for forecasting weather conditions and predicting rainfall by means of machine learning. Here, there are two set ups: one, to measure the weather parameters like temperature, humidity using sensors along with Arduino and another set up, to display the current values(status) and predicted rainfall based on the trained machine learning data sets. The weather forecasting and prediction is done based on the older datasets collected and compared with the current values. The user need not have a backup of huge data to predict the rainfall. Instead a machine learning algorithm can suffice the same. The temperature, humidity sensor modules are used to measure weather parameters and interfaced to an Arduino controller. The proposed setup will compare the forecast value with real-time data, and the predict rainfall based on the dataset fed to the machine learning algorithm.

scholarly journals Detection of untreated sewage discharges to watercourses using machine learning

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Peter Hammond ◽  
Michael Suttie ◽  
Vaughan T. Lewis ◽  
Ashley P. Smith ◽  
Andrew C. Singer
Keyword(s):  
Machine Learning ◽  
Wastewater Treatment ◽  
Wastewater Treatment Plants ◽  
Training Data ◽  
Regulatory Oversight ◽  
Untreated Wastewater ◽  
Untreated Sewage ◽  
Water Companies ◽  
Analytical Approaches ◽  
Sewage Discharges

AbstractMonitoring and regulating discharges of wastewater pollution in water bodies in England is the duty of the Environment Agency. Identification and reporting of pollution events from wastewater treatment plants is the duty of operators. Nevertheless, in 2018, over 400 sewage pollution incidents in England were reported by the public. We present novel pollution event reporting methodologies to identify likely untreated sewage spills from wastewater treatment plants. Daily effluent flow patterns at two wastewater treatment plants were supplemented by operator-reported incidents of untreated sewage discharges. Using machine learning, known spill events served as training data. The probability of correctly classifying a randomly selected pair of ‘spill’ and ‘no-spill’ effluent patterns was above 96%. Of 7160 days without operator-reported spills, 926 were classified as involving a ‘spill’. The analysis also suggests that both wastewater treatment plants made non-compliant discharges of untreated sewage between 2009 and 2020. This proof-of-principle use of machine learning to detect untreated wastewater discharges can help water companies identify malfunctioning treatment plants and inform agencies of unsatisfactory regulatory oversight. Real-time, open access flow and alarm data and analytical approaches will empower professional and citizen scientific scrutiny of the frequency and impact of untreated wastewater discharges, particularly those unreported by operators.

Urban Finescale Forecasting Reveals Weather Conditions with Unprecedented Detail

2017 ◽  
Vol 98 (12) ◽  
pp. 2675-2688 ◽  
Author(s):  
R. J. Ronda ◽  
G. J. Steeneveld ◽  
B. G. Heusinkveld ◽  
J. J. Attema ◽  
A. A. M. Holtslag
Keyword(s):  
Local Governments ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Morphological Characteristics ◽  
Weather Conditions ◽  
Human Thermal Comfort ◽  
Forecasting System ◽  
The Impact ◽  
Urban Weather ◽  
Neighborhood Level

Abstract Urban landscapes impact the lives of urban dwellers by influencing local weather conditions. However, weather forecasting down to the street and neighborhood scale has been beyond the capabilities of numerical weather prediction (NWP) despite the fact that observational systems are now able to monitor urban climate at these scales. In this study, weather forecasts at intra-urban scales were achieved by exploiting recent advances in topographic element mapping and aerial photography as well as looking at detailed mappings of soil characteristics and urban morphological properties, which were subsequently incorporated into a specifically adapted Weather Research and Forecasting (WRF) Model. The urban weather forecasting system (UFS) was applied to the Amsterdam, Netherlands, metropolitan area during the summer of 2015, where it produced forecasts for the city down to the neighborhood level (a few hundred meters). Comparing these forecasts to the dense network of urban weather station observations within the Amsterdam metropolitan region showed that the forecasting system successfully determined the impact of urban morphological characteristics and urban spatial structure on local temperatures, including the cooling effect of large water bodies on local urban temperatures. The forecasting system has important practical applications for end users such as public health agencies, local governments, and energy companies. It appears that the forecasting system enables forecasts of events on a neighborhood level where human thermal comfort indices exceeded risk thresholds during warm weather episodes. These results prove that worldwide urban weather forecasting is within reach of NWP, provided that appropriate data and computing resources become available to ensure timely and efficient forecasts.

scholarly journals ANN-Based Soft Sensor to Predict Effluent Violations in Wastewater Treatment Plants

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1280 ◽  
Author(s):  
Ivan Pisa ◽  
Ignacio Santín ◽  
Jose Vicario ◽  
Antoni Morell ◽  
Ramon Vilanova
Keyword(s):  
Wastewater Treatment ◽  
Short Term Memory ◽  
Wastewater Treatment Plants ◽  
Control Strategies ◽  
False Positive Rate ◽  
Soft Sensor ◽  
Aquatic Life ◽  
Pollutant Concentrations ◽  
High Concentrations ◽  
Artificial Neural Network Ann

Wastewater treatment plants (WWTPs) form an industry whose main goal is to reduce water’s pollutant products, which are harmful to the environment at high concentrations. In addition, regulations are applied by administrations to limit pollutant concentrations in effluent. In this context, control strategies have been adopted by WWTPs to avoid violating these limits; however, some violations still occur. For that reason, this work proposes the deployment of an artificial neural network (ANN)-based soft sensor in which a Long-Short Term Memory (LSTM) network is used to generate predictions of nitrogen-derived components, specifically ammonium ( S N H ) and total nitrogen ( S N t o t ). S N t o t is a limiting nutrient and can therefore cause eutrophication, while nitrogen in the S N H form is toxic to aquatic life. These parameters are used by control strategies to allow actions to be taken in advance and only when violations are predicted. Since predictions complement control strategies, the evaluation of the ANN-based soft sensor was carried out using the Benchmark Simulation Model N.2. (BSM2) and three different control strategies (from low to high control complexity). Results show that our proposed method is able to predict nitrogen-derived products with good accuracy: the probability of detecting violations of BSM2’s limits is 86%–94%. Moreover, the prediction accuracy can be improved by calibrating the soft sensor; for example, perfect prediction of all future violations can be achieved at the expense of increasing the false positive rate.

Integrated soft sensor model for flow control

2006 ◽  
Vol 53 (4-5) ◽  
pp. 473-482 ◽  
Author(s):  
G. Äijälä ◽  
D. Lumley
Keyword(s):  
Wastewater Treatment ◽  
Control System ◽  
Flow Control ◽  
Wastewater Treatment Plants ◽  
Treatment Plant ◽  
Soft Sensor ◽  
Plant Performance ◽  
Unit Process ◽  
Flow Models ◽  
Flow Control System

Tighter discharge permits often require wastewater treatment plants to maximize utilization of available facilities in order to cost-effectively reach these goals. Important aspects are minimizing internal disturbances and using available information in a smart way to improve plant performance. In this study, flow control throughout a large highly automated wastewater treatment plant (WWTP) was implemented in order to reduce internal disturbances and to provide a firm foundation for more advanced process control. A modular flow control system was constructed based on existing instrumentation and soft sensor flow models. Modules were constructed for every unit process in water treatment and integrated into a plant-wide model. The flow control system is used to automatically control recirculation flows and bypass flows at the plant. The system was also successful in making accurate flow estimations at points in the plant where it is not possible to have conventional flow meter instrumentation. The system provides fault detection for physical flow measuring devices. The module construction allows easy adaptation for new unit processes added to the treatment plant.

scholarly journals A Machine-Learning Approach Combining Wavelet Packet Denoising with Catboost for Weather Forecasting

Atmosphere ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1618
Author(s):  
Dan Niu ◽  
Li Diao ◽  
Zengliang Zang ◽  
Hongshu Che ◽  
Tianbao Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Weather Forecasting ◽  
Human Life ◽  
Wavelet Packet ◽  
Weather Prediction ◽  
Convergence Time ◽  
Observation Data ◽  
Initial State ◽  
Historical Observation ◽  
Meteorological Elements

Accurate forecasting of future meteorological elements is critical and has profoundly affected human life in many aspects from rainstorm warning to flight safety. The conventional numerical weather prediction (NWP) sometimes leads to unsatisfactory performance due to inappropriate initial state settings. In this paper, a short-term weather forecasting model based on wavelet packet denoising and Catboost is proposed, which takes advantage of the fusion information combining the historical observation data with the prior knowledge from NWP. The feature selection and spatiotemporal feather addition are also explored to further improve performance. The proposed method is evaluated on the datasets provided by Beijing weather stations. Experimental results demonstrate that compared with many deep-learning or machine-learning methods such as LSTM, Seq2Seq, and random forest, the proposed Catboost model incorporated with wavelet packet denoising can achieve shorter convergence time and higher prediction accuracy.

scholarly journals Support vector machines for environmental informatics : application to biological nitrogen removal in wastewater treatment plants

2021 ◽  
Author(s):  
Yinghui Yang
Keyword(s):  
Wastewater Treatment ◽  
Nitrogen Removal ◽  
Wastewater Treatment Plants ◽  
Control Strategies ◽  
Weather Conditions ◽  
Support Vector ◽  
Biological Nitrogen Removal ◽  
Complex Nature ◽  
Generalized Regression Neural Network ◽  
Biological Nitrogen

In order to meet the more stringent environmental regulations, the adaptive and optimal control strategies should be investigated for the biological nitrogen removal (BNR) processes in wastewater treatment plants. Because of the complex nature of the microbial metabolism involved, the conventional mechanistic models for nitrogen removal are difficult to formulate and the existing ones are still uncertain to some extent. Alternatively, the machine learning methods have been investigated as black-box modelling techniques. A new approach, Support Vector Machine (SVM) was proposed to be used to model the biological nitrogen removal processes in this thesis. Specifically, LS-SVM, a simplified formulation of SVM, was applied to predict the concentration of nitrate & nitrite (NO). The simulation results indicate that the proposed method has better generalization performance in comparison with generalized regression neural network, especially under weather conditions that are quite different from the training weather conditions.

scholarly journals The Use of Gridded Model Output Statistics (GMOS) in Energy Forecasting of a Solar Car

Energies ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 1984
Author(s):  
Christiaan Oosthuizen ◽  
Barend Van Wyk ◽  
Yskandar Hamam ◽  
Dawood Desai ◽  
Yasser Alayli
Keyword(s):  
Weather Forecasting ◽  
Forecast Error ◽  
Weather Prediction ◽  
Weather Conditions ◽  
Model Output ◽  
Energy Forecasting ◽  
Weather Forecasts ◽  
Improved Performance ◽  
Model Output Statistics ◽  
Auxiliary Services

For many years, primary weather forecasting services (Global Forecast System (GFS) and the European Centre for Medium-Range Weather Forecasts (ECMWF)) have been made available to the public through global Numerical Weather Prediction (NWP) models estimating a multitude of general weather variables in a variety of resolutions. Secondary services such as weather experts Meteomatics AG use data and improve the forecasts through various methods. They tailor for the specific needs of customers in the wind and solar power generation sector as well as data scientists, analysts, and meteorologists in all areas of business. These auxiliary services have improved performance and provide reliable data. However, this work extended these auxiliary services to so-called tertiary services in which the weather forecasts were further conditioned for the very niche application environment of mobile solar technology in solar car energy management. The Gridded Model Output Statistics (GMOS) Global Horizontal Irradiance (GHI) model developed in this work utilizes historical data from various ground station locations in South Africa to reduce the mean forecast error of the GHI component. An average Root Mean Square Error (RMSE) improvement of 11.28% was shown across all locations and weather conditions. It was also shown how the incorporation of the GMOS model could have increased the accuracy in regard to the State of Charge (SoC) energy simulation of a solar car during the Sasol Solar Challenge 2018 and the possible range benefits thereof.

Machine Learning Method for Forecasting Weather Needed For Crop Water Demand Estimations in Low-Resource Settings Using A Case Study in Morocco

2021 ◽  
Author(s):  
Carolyn Sheline ◽  
Amos Winter
Keyword(s):  
Machine Learning ◽  
Water Demand ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Crop Productivity ◽  
Irrigation Scheduling ◽  
Crop Water ◽  
Low Resource Settings ◽  
Low Resource ◽  
Crop Water Demand

Abstract Low and middle income countries often do not have the infrastructure needed to support weather forecasting models, which are computationally expensive and often require detailed inputs from local weather stations. Local, low-cost weather prediction services are needed to enable optimal irrigation scheduling and increase crop productivity for rural farmers in low-resource settings. This work proposes a machine learning approach to predict the weather inputs needed to calculate crop water demand, namely evapotranspiration and precipitation. The focus of this work is on the accuracy with which Moroccan weather can be predicted with a vector autoregression (VAR) model compared to using typical meteorological year (TMY) weather, and how this accuracy changes as the number of weather parameters is reduced.

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