Development of a Digital ESP Performance Monitoring System Based on Artificial Intelligence

2021 ◽  
Author(s):  
Göktug Diker ◽  
Herwig Frühbauer ◽  
Edna Michelle Bisso Bi Mba
Keyword(s):  
Machine Learning ◽  
Project Management ◽  
Data Science ◽  
Performance Monitoring ◽  
Intelligent System ◽  
Operating Conditions ◽  
Oil Field ◽  
Training Dataset ◽  
Detailed Knowledge ◽  
Concept Evaluation

Abstract Wintershall Dea is developing together with partners a digital system to monitor and optimize electrical submersible pump (ESP) performance based on the data from Mittelplate oil field. This tool is using machine learning (ML) models which are fed by historic data and will notify engineers and operators when operating conditions are trending beyond the operating envelope, which enables an operator to mitigate upcoming performance problems. In addition to traditional engineering methods, such a system will capture knowledge by continuous improvement based on ML. With this approach the engineer has a system at hand to support the day-to-day work. Manual monitoring and on demand investigations are now backed up by an intelligent system which permanently monitors the equipment. In order to create such a system, a proof of concept (PoC) study has been initiated with industry partners and data scientists to evaluate historic events, which are used to train the ML-systems. This phase aims to better understand the capabilities of machine learning and data science in the subsurface domain as well as to build up trust for the engineers with such systems. The concept evaluation has shown that the intensive collaboration between engineers and data scientist is essential. A continuous and structured exchange between engineering and data science resulted in a mutual developed product, which fits the engineer's needs based on the technical capabilities and limits set by ML-models. To organize such a development, new project management elements like agile working methods, sprints and scrum methods were utilized. During the development Wintershall Dea has partnered with two organizations. One has a pure data science background and the other one was the data science team of the ESP manufacturer. After the PoC period the following conclusions can be derived: (1) data quality and format is key to success; (2) detailed knowledge of the equipment speeds up the development and the quality of the results; (3) high model accuracy requires a high number of events in the training dataset. The overall conclusion of this PoC is that the collaboration between engineers and data scientists, fostered by the agile project management toolkit and suitable datasets, leads to a successful development. Even when the limits of the ML-algorithms are hit, the model forecast, in combination with traditional engineering methods, adds significant value to the ESP performance. The novelty of such a system is that the production engineer will be supported by trusted ML-models and digital systems. This system in combination with the traditional engineering tools improves monitoring of the equipment and taking decisions leading to increased equipment performance.

Intelligent Molecular Identification for High Performance Organosulfide Capture Using Active Machine Learning Algorithm

2021 ◽  
Author(s):  
Yuxiang Chen ◽  
Chuanlei Liu ◽  
Yang An ◽  
Yue Lou ◽  
Yang Zhao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Similarity Search ◽  
Data Science ◽  
Molecular Similarity ◽  
Molecular Design ◽  
Supervised Machine Learning ◽  
Training Dataset ◽  
Methyl Mercaptan ◽  
Model Framework ◽  
Modeling Framework

Machine learning and computer-aided approaches significantly accelerate molecular design and discovery in scientific and industrial fields increasingly relying on data science for efficiency. The typical method used is supervised learning which needs huge datasets. Semi-supervised machine learning approaches are effective to train unlabeled data with improved modeling performance, whereas they are limited by the accumulation of prediction errors. Here, to screen solvents for removal of methyl mercaptan, a type of organosulfur impurities in natural gas, we constructed a computational framework by integrating molecular similarity search and active learning methods, namely, molecular active selection machine learning (MASML). This new model framework identifies the optimal molecules set by molecular similarity search and iterative addition to the training dataset. Among all 126,068 compounds in the initial dataset, 3 molecules were identified to be promising for methyl mercaptan (MeSH) capture, including benzylamine (BZA), p-methoxybenzylamine (PZM), and N,N-diethyltrimethylenediamine (DEAPA). Further experiments confirmed the effectiveness of our modeling framework in efficient molecular design and identification for capturing methyl mercaptan, in which DEAPA presents a Henry's law constant 89.4% lower than that of methyl diethanolamine (MDEA).

A Fuzzy Model with Thermodynamic Based Consequents and a Niching Swarm-Based Supervisor to Capture the Uncertainties of Damavand Power System

Fuzzy Systems ◽  
2017 ◽  
pp. 292-307
Author(s):  
Ahmad Mozaffari ◽  
Moein Mohammadpour ◽  
Alireza Fathi ◽  
Mofid Gorji-Bandpy
Keyword(s):  
Machine Learning ◽  
Power System ◽  
Intelligent System ◽  
Fuzzy Model ◽  
Operating Conditions ◽  
Operating Parameters ◽  
Fuzzy Approach ◽  
Swarm Optimization ◽  
Learning Mechanism ◽  
High Degree

In this investigation, a novel fuzzy mathematical program based on thermodynamic principles is implemented to capture the uncertainties of a practical power system, known as Damavand power plant. The proposed intelligent machine takes the advantages of a niching bio-inspired learning mechanism to be reconciled to the requirements of the problem at hand. The aim of the bio-inspired fuzzy based intelligent system is to yield a model capable of recognizing different operating parameters of Damavand power system under different operating conditions. To justify the privileges of using a niching metaheuristic over gradient descend methods, the authors use the data, derived through data acquisition, together with a machine learning based approach to estimate the multi-modality associated with the training of the proposed fuzzy model. Moreover, the niching bio-inspired metaheuristic, niching particle swarm optimization (NPSO), is compared to canonical PSO (CPSO), stochastic social PSO (SSPSO), unified PSO (UPSO), comprehensive learning PSO (CLPSO), PSO with constriction factor (PSOCF) and fully informed PSO (FIPSO). Through experiments and analysis of the characteristics of the problem being optimized, it is proved that NPSO is not only able to tackle the deficiencies of the learning process, but also can effectively adjust the fuzzy approach to conduct the identification process with a high degree of robustness and accuracy.

Classification of different skarn deposits based on the compositional variability of associated grandite garnets: a data science and Machine Learning approach

2021 ◽  
Author(s):  
Urmi Ghosh ◽  
Tuhin Chakraborty
Keyword(s):  
Machine Learning ◽  
Trace Element ◽  
Data Science ◽  
Training Dataset ◽  
Support Vector ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Skarn Deposits

<p>Rapid technological improvements made in in-situ analysis techniques, including LA-ICPMS, have transformed the field of analytical geochemistry. This has a far-reaching impact for different petrogenetic and ore-genetic studies where minute major and trace element compositional changes between different mineral zones within a single crystal can now be demarcated. Minerals such as garnet although robust are quite sensitive to the changing P-T and fluid conditions during their formation. These minerals have become powerful tools to characterize mineralization types. Previously, Meinert (1992) has used in-situ major element EPMA analysis results to classify different skarn deposit based on the end-member composition of hydrothermal garnets. Alternatively, Tian et al. (2019) used the garnet trace element composition for the similar purpose. However, these discrimination plots/ classification schemes show major overlap in different skarn deposits, such as Fe, Cu, Zn, and Au. The present study is an attempt to use machine learning approach on available garnet data to found a more potent classification scheme for skarn deposits, thus reaffirming garnet as a faithful indicator for hydrothermal ore deposits. We have meticulously collected major and trace element data of Ca-rich garnets, associated with different skarn deposits worldwide from 40 publications. This collected data is then used to train a model for fingerprinting the skarn deposits. Stratified random sampling method has been used on the dataset with 80% of the samples as test set and the rest 20 % as training dataset. We have used K-nearest neighbour (KNN), Support Vector Machine (SVM) and Random Forest algorithms on the data by using Python as a platform. These ML classification algorithm performs better than the earlier existing models available for classification of ore types based on garnet composition in skarn system. Factor importance is calculated that shows which elements play a pivotal role in classification of the ore type. Our results depict that multiple garnet forming elements taken together can reliably be used to discriminate between different ore formation settings.</p>

Machine Learning and Data Science Project Management From an Agile Perspective

2022 ◽  
pp. 73-88
Author(s):  
Murat Pasa Uysal
Keyword(s):  
Machine Learning ◽  
Project Management ◽  
Data Science ◽  
Business Models ◽  
Background Information ◽  
Future Research ◽  
Initial Attempt ◽  
Agile Project Management ◽  
Experimental Implementation ◽  
Management Method

Successful implementations of machine learning (ML) and data science (DS) applications have enabled innovative business models and brought new opportunities for organizations. On the other hand, research studies report that organizations employing ML and DS solutions are at a high risk of failure and they can easily fall short of their objectives. One major factor is to adopt or tailor a project management method for the specific requirements of ML and DS applications. Therefore, agile project management (APM) may be proposed as a solution. However, there is significantly less study that explores ML and DS project management from an agile perspective. In this chapter, the authors discuss methods and challenges according to the background information and practice areas of ML, DS, and APM. This study can be viewed as an initial attempt to enhance these knowledge and practice domains in view of APM. Therefore, future research efforts will focus on the challenges as well as the experimental implementation of APM methods in real industrial case studies of ML and DS.

scholarly journals Navigating Virtual Environments Using Leg Poses and Smartphone Sensors

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 299 ◽  
Author(s):  
Georgios Tsaramirsis ◽  
Seyed Buhari ◽  
Mohammed Basheri ◽  
Milos Stojmenovic
Keyword(s):  
Machine Learning ◽  
Virtual Environments ◽  
Sensory Information ◽  
Operating Conditions ◽  
Identification Accuracy ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Training Dataset ◽  
Learning Techniques ◽  
The Right

Realization of navigation in virtual environments remains a challenge as it involves complex operating conditions. Decomposition of such complexity is attainable by fusion of sensors and machine learning techniques. Identifying the right combination of sensory information and the appropriate machine learning technique is a vital ingredient for translating physical actions to virtual movements. The contributions of our work include: (i) Synchronization of actions and movements using suitable multiple sensor units, and (ii) selection of the significant features and an appropriate algorithm to process them. This work proposes an innovative approach that allows users to move in virtual environments by simply moving their legs towards the desired direction. The necessary hardware includes only a smartphone that is strapped to the subjects’ lower leg. Data from the gyroscope, accelerometer and campus sensors of the mobile device are transmitted to a PC where the movement is accurately identified using a combination of machine learning techniques. Once the desired movement is identified, the movement of the virtual avatar in the virtual environment is realized. After pre-processing the sensor data using the box plot outliers approach, it is observed that Artificial Neural Networks provided the highest movement identification accuracy of 84.2% on the training dataset and 84.1% on testing dataset.

Machine learning to automate rapid soil health assessment using infrared spectroscopy

2021 ◽  
Author(s):  
Leonardo Deiss ◽  
Shameema Oottikkal ◽  
Karen Tomko ◽  
Wanyu Huang ◽  
Steve Culman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Infrared Spectroscopy ◽  
Soil Properties ◽  
Data Science ◽  
Intelligent System ◽  
Soil Health ◽  
Health Indicators ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector

<p>Soil infrared spectroscopy has great potential for estimating soil properties, but reference soil measurements are typically required in combination with multivariate statistical models to estimate soil properties. User-friendly predictive tools based on open-source statistical environment remain one of the main limitations to enable technology diffusion to non-specialist users. Our aim is to build capacity for an automated machine learning routine for rapid and robust prediction of soil health indicators using lab acquired soil infrared spectra. This intelligent system runs on R statistical environment and includes (1) a diverse soil spectral library comprising main physiographic regions from the USA Midwest region under diverse land uses and various sampling depths, (2) a classification process to detect potential outliers in newly acquired spectra using supervised machine learning techniques, and (3) a multi-model optimized prediction process based on linear and non-linear statistical procedures (partial least squares, support vector machines, and neural network). This prediction system works at the intersection of soil and data science and high-performance computing to enable efficient parallel processing of spectral data on multi-core coprocessors. Using artificial intelligence to automate soil infrared spectroscopy is a fundamental demand that will make this technique an effective routine in soil laboratories to estimate soil health.</p>

Intelligent system of English composition scoring model based on improved machine learning algorithm

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Assessment System ◽  
English Composition ◽  
Region Extraction ◽  
Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

Adsorption Isotherm Predictions for Multiple Molecules in MOFs Using the Same Deep Learning Model

2019 ◽  
Author(s):  
Ryther Anderson ◽  
Achay Biong ◽  
Diego Gómez-Gualdrón
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Molecular Simulation ◽  
Large Scale ◽  
Learning Model ◽  
Operating Conditions ◽  
Small Subset ◽  
Screening Methods ◽  
Large Set ◽  
Metal Organic

<div>Tailoring the structure and chemistry of metal-organic frameworks (MOFs) enables the manipulation of their adsorption properties to suit specific energy and environmental applications. As there are millions of possible MOFs (with tens of thousands already synthesized), molecular simulation, such as grand canonical Monte Carlo (GCMC), has frequently been used to rapidly evaluate the adsorption performance of a large set of MOFs. This allows subsequent experiments to focus only on a small subset of the most promising MOFs. In many instances, however, even molecular simulation becomes prohibitively time consuming, underscoring the need for alternative screening methods, such as machine learning, to precede molecular simulation efforts. In this study, as a proof of concept, we trained a neural network as the first example of a machine learning model capable of predicting full adsorption isotherms of different molecules not included in the training of the model. To achieve this, we trained our neural network only on alchemical species, represented only by their geometry and force field parameters, and used this neural network to predict the loadings of real adsorbates. We focused on predicting room temperature adsorption of small (one- and two-atom) molecules relevant to chemical separations. Namely, argon, krypton, xenon, methane, ethane, and nitrogen. However, we also observed surprisingly promising predictions for more complex molecules, whose properties are outside the range spanned by the alchemical adsorbates. Prediction accuracies suitable for large-scale screening were achieved using simple MOF (e.g. geometric properties and chemical moieties), and adsorbate (e.g. forcefield parameters and geometry) descriptors. Our results illustrate a new philosophy of training that opens the path towards development of machine learning models that can predict the adsorption loading of any new adsorbate at any new operating conditions in any new MOF.</div>

Data science in economics: comprehensive review of advanced machine learning and deep learning methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Ferdinand Filip ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
State Of The Art ◽  
Science Methods ◽  
Learning Models ◽  
Diverse Range ◽  
Hybrid Machine ◽  
Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
Learning Methods ◽  
Comprehensive Review
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