Offshore Water Treatment KPIs Using Machine Learning Techniques

Process Conditions ◽

Operation Conditions ◽

Sulfate Removal ◽

Abstract New water treatment facilities in the Gulf of Mexico include a seawater Sulfate Removal Unit (SRU) to mitigate reservoir souring and scaling. The general industry sulfate target for offshore SRU is usually 20 mg/L or even 40 mg/L; however, some facilities may require <10 mg/L of sulfate in injection water, which makes water quality monitoring more critical and challenging. Current industrial practice relies on only pressure drop and a constant cleaning interval frequency to perform SRU maintenance which may result in reduced membrane life due to frequency cleaning or severe membrane fouling without the capability to predict fouling based on process conditions. The machine learning techniques applied will fill the gap and deliver a prediction model based on both simulation and real-time field data. This model will track and monitor the system key performance indicators (KPIs) including pressure, membrane fouling factor (FF), permeate sulfate concentration etc. The monitoring and prediction of these KPIs provide estimates on when the next maintenance procedure is required, track membrane system status for troubleshooting and actions, and optimize membrane performance by tuning operation conditions.

Use of Learning Mechanisms to Improve the Condition Monitoring of Wind Turbine Generators: A Review

Energies ◽

10.3390/en14217129 ◽

2021 ◽

Vol 14 (21) ◽

pp. 7129

Author(s):

Ana Rita Nunes ◽

Hugo Morais ◽

Alberto Sardinha

Keyword(s):

Machine Learning ◽

Condition Monitoring ◽

Predictive Maintenance ◽

Learning Mechanisms ◽

Learning Stage ◽

Operation Conditions ◽

Turbine Generators ◽

Wind Turbine Generators ◽

The main goal of this paper is to review and evaluate how we can take advantage of state-of-the-art machine learning techniques and apply them in wind energy operation conditions monitoring and fault diagnosis, boosting wind turbines’ availability. To accomplish this, we focus our work on analysing the current techniques in predictive maintenance, which are aimed at acting before a major failure occurs using condition monitoring. In particular, we start framing the predictive maintenance problem as an ML problem to detect patterns that indicate a fault on turbine generators. Then, we extend the problem to detect future faults. Therefore, this review will consist of analysing techniques to tackle the challenges of each machine learning stage, such as data pre-processing, feature engineering, and the selection of the best-suited model. By using specific evaluation metrics, the expected final result of using these techniques will be an improvement in the early prediction of a future fault. This improvement will have an increase in the availability of the turbine, and therefore in energy production.

Water quality monitoring: from conventional to emerging technologies

Water Science & Technology Water Supply ◽

10.2166/ws.2019.144 ◽

2019 ◽

Vol 20 (1) ◽

pp. 28-45

Author(s):

Umair Ahmed ◽

Rafia Mumtaz ◽

Hirra Anwar ◽

Sadaf Mumtaz ◽

Ali Mustafa Qamar

Keyword(s):

Machine Learning ◽

Water Quality ◽

Industrial Development ◽

Low Cost ◽

Research Work ◽

Water Quality Monitoring ◽

Rapid Urbanization ◽

Analysis Process ◽

Abstract The rapid urbanization and industrial development have resulted in water contamination and water quality deterioration at an alarming rate, deeming its quick, inexpensive and accurate detection imperative. Conventional methods to measure water quality are lengthy, expensive and inefficient, including the manual analysis process carried out in a laboratory. The research work in this paper focuses on the problem from various perspectives, including the traditional methods of determining water quality to gain insight into the problem and the analysis of state-of-the-art technologies, including Internet of Things (IoT) and machine learning techniques to address water quality. After analyzing the currently available solutions, this paper proposes an IoT-based low-cost system employing machine learning techniques to monitor water quality in real time, analyze water quality trends and detect anomalous events such as intentional contamination of water.

Modeling trihalomethanes concentrations in water treatment plants using machine learning techniques

10.5004/dwt.2018.22353 ◽

2018 ◽

Vol 111 ◽

pp. 125-133 ◽

Cited By ~ 2

Author(s):

Jongkwan Park ◽

Chan ho Lee ◽

Kyung Hwa Cho ◽

Seongho Hong ◽

Young Mo Kim ◽

...

Keyword(s):

Machine Learning ◽

Water Treatment ◽

Water Treatment Plants ◽

Advanced control of membrane fouling in filtration systems using artificial intelligence and machine learning techniques: A critical review

Process Safety and Environmental Protection ◽

10.1016/j.psep.2019.01.013 ◽

2019 ◽

Vol 123 ◽

pp. 229-252 ◽

Cited By ~ 17

Author(s):

Majid Bagheri ◽

Ali Akbari ◽

Sayed Ahmad Mirbagheri

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Membrane Fouling ◽

Critical Review ◽

Learning Techniques ◽

Advanced Control

Water Quality Monitoring and Prediction of Water Quality at College Premises using Internet of Things

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.e1014.0785s319 ◽

2019 ◽

Vol 8 (5S3) ◽

pp. 53-57

Keyword(s):

Machine Learning ◽

Water Quality ◽

Random Forest ◽

Real Time ◽

Multilayer Perceptron ◽

Low Cost ◽

Water Quality Monitoring ◽

Quality Monitoring ◽

IoT is becoming more popular and effective tool for any real time application. It has been involved for various water quality monitoring system to maintain the water hygiene level. The main objective is to build a system that regularly monitors the water quality and manages the sustainability. This system deals with specific standards like low cost background and system efficiency when compared to other studies. In this paper, IoT based real time monitoring of water quality system is implemented along with Machine learning techniques such as J48, Multilayer Perceptron (MLP), and Random Forest. These machine learning techniques are compared based on the hyper-parameters and the results were obtained. The attributes such as pH, Dissolved Oxygen (DO), turbidity, conductivity obtained from the corresponding sensors are used to create a prediction model which classifies the quality of water. Measurement of water quality and reporting system is implemented by using Arduino controller, GSM/GPRS module for gathering data in real time. The collected data are then analyzed using WEKA interface which is a visualization tool used for the analysis of data and prediction modeling.The Random forest technique outperforms J48 and Multilayer perceptron by giving 98.89% of correctly classified instances.

Dissolved oxygen modeling of an urban stream using grid partitioning and subtractive clustering fuzzy techniques

10.22541/au.161803044.45227349/v1 ◽

2021 ◽

Author(s):

Sameer Arora

Keyword(s):

Machine Learning ◽

Water Quality ◽

Water Treatment ◽

Dissolved Oxygen ◽

Quality Parameters ◽

Water Quality Parameters ◽

Subtractive Clustering ◽

Grid Partitioning ◽

Dissolved oxygen is one of the prime parameters for assessing the water quality of any stream and the health status of aquatic life. The dissolved oxygen present in the water body plays an essential role in deciding water treatment processes to enhance water quality up to the design standards for the specified water use. Thus, the accurate estimation of dissolved oxygen concentration is necessary to evolve measures for maintaining the riverine ecosystem and designing the appropriate water treatment plants. Machine learning techniques are becoming useful tools for the prediction and simulation of water quality parameters. With these viewpoints, a study was carried out in the Delhi stretch of Yamuna River, India, and physiochemical parameters were examined for five years to simulate the dissolved oxygen using different machine learning techniques. Simulation and prediction competencies of ANFIS grid partitioning (ANFIS-GP) and ANFIS subtractive clustering (ANFIS-SC) were tested on various water quality parameters. Variation in dissolved oxygen was examined on various combinations of parameters. ANFIS-GP has been designed using the Gaussian function, and ANFIS-SC works on the likelihood of cluster centers. Results obtained from the models were evaluated using root mean square error (RMSE) and coefficient of determination (R2) to identify the optimum solution and appropriate combination of parameters that simulate the observed dissolved oxygen. Results of ANFIS-GP and ANFIS-SC indicate that both the models produce suitable solutions for the prediction; however, ANFIS-GP outperforms the ANFIS-SC and could act as a useful tool for defining, planning, and management of water quality parameters.

Using machine learning techniques to reduce data annotation time

PsycEXTRA Dataset ◽

10.1037/e577762012-020 ◽

2006 ◽

Author(s):

Christopher Schreiner ◽

Kari Torkkola ◽

Mike Gardner ◽

Keshu Zhang

Keyword(s):

Machine Learning ◽

Data Annotation ◽

Using Machine Learning Algorithms on Prediction of Stock Price

Journal of Modeling and Optimization ◽

10.32732/jmo.2020.12.2.84 ◽

2020 ◽

Vol 12 (2) ◽

pp. 84-99

Author(s):

Li-Pang Chen

Keyword(s):

Machine Learning ◽

Stock Price ◽

Short Term Memory ◽

Machine Learning Algorithms ◽

Support Vector ◽

Short Term ◽

Learning Techniques ◽

Historical Database ◽

Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.