Introduction to machine learning and Python

Belyadi, Hoss; Haghighat, Alireza

doi:10.1016/b978-0-12-821929-4.00006-8

Cited by 24 publications

(35 citation statements)

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“…A RF is known as an ensemble algorithm since it combines many DT models into a single model. The detailed discussion of the RF regression can be found in ref ( 62 ).…”

Section: Methodsmentioning

confidence: 99%

Machine Learning-Based Accelerated Approaches to Infer Breakdown Pressure of Several Unconventional Rock Types

et al. 2022

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Unconventional oil and gas reservoirs are usually classified by extremely low porosity and permeability values. The most economical way to produce hydrocarbons from such reservoirs is by creating artificially induced channels. To effectively design hydraulic fracturing jobs, accurate values of rock breakdown pressure are needed. Conducting hydraulic fracturing experiments in the laboratory is a very expensive and timeconsuming process. Therefore, in this study, different machine learning (ML) models were efficiently utilized to predict the breakdown pressure of tight rocks. In the first part of the study, to measure the breakdown pressures, a comprehensive hydraulic fracturing experimental study was conducted on various rock specimens. A total of 130 experiments were conducted on different rock types such as shales, sandstone, tight carbonates, and synthetic samples. Rock mechanical properties such as Young's modulus (E), Poisson's ratio (ν), unconfined compressive strength, and indirect tensile strength (σ t ) were measured before conducting hydraulic fracturing tests. ML models were used to correlate the breakdown pressure of the rock as a function of fracturing experimental conditions and rock properties. In the ML model, we considered experimental conditions, including the injection rate, overburden pressures, and fracturing fluid viscosity, and rock properties including Young's modulus (E), Poisson's ratio (ν), UCS, and indirect tensile strength (σ t ), porosity, permeability, and bulk density. ML models include artificial neural networks (ANNs), random forests, decision trees, and the K-nearest neighbor. During training of ML models, the model hyperparameters were optimized by the grid-search optimization approach. With the optimal setting of the ML models, the breakdown pressure of the unconventional formation was predicted with an accuracy of 95%. The accuracy of all ML techniques was quite similar; however, an explicit empirical correlation from the ANN technique is proposed. The empirical correlation is the function of all input features and can be used as a standalone package in any software. The proposed methodology to predict the breakdown pressure of unconventional rocks can minimize the laboratory experimental cost of measuring fracture parameters and can be used as a quick assessment tool to evaluate the development prospect of unconventional tight rocks.

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“…A RF is known as an ensemble algorithm since it combines many DT models into a single model. The detailed discussion of the RF regression can be found in ref ( 62 ).…”

Section: Methodsmentioning

confidence: 99%

Machine Learning-Based Accelerated Approaches to Infer Breakdown Pressure of Several Unconventional Rock Types

et al. 2022

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“…• Able to accurately make economic and operational prediction based on input variables (Belyadi and Haghighat, 2021).…”

Section: )mentioning

confidence: 99%

“…ML is one of the most well-known subsets of AI, which is popularized by its ability to self-develop via learning of new knowledge (Woolf, 2009;Holzinger et al, 2018;Helm et al, 2020). The ability of ML to self-develop allows it to study patterns within a data set (Belyadi and Haghighat, 2021;Chanal et al, 2021). This enables it to make predictions and forecast the optimal method or condition for optimal performance (Edgar and Manz, 2017;Doshi and Varghese, 2022).…”

Section: Machine Learning (Ml)mentioning

confidence: 99%

Future era of techno-economic analysis: Insights from review

et al. 2022

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Techno-economic analysis (TEA) has been considered an important tool to evaluate the economic performance of industrial processes. Recently, the application of TEA has been observed to have exponential growth due to the increasing competition among businesses across various industries. Thus, this review presents a deliberate overview of TEA to inculcate the importance and relevance of TEA. To further support the aforementioned points, this review article starts with a bibliometric analysis to evaluate the applicability of TEA within the research community. Conventional TEA is widely known to be conducted via software modeling (i.e., Python, AMIS, MATLAB, Aspen HYSYS, Aspen Plus, HOMER Pro, FORTRAN, R, SysML and Microsoft Excel) without involving any correlation or optimization between the process and economic performance. Apart from that, due to the arrival of the industrial revolution (IR) 4.0, industrial processes are being revolutionized into smart industries. Thus, to retain the integrity of TEA, a similar evolution to smart industries is deemed necessary. Studies have begun to incorporate data-driven technologies (i.e., artificial intelligence (AI) and blockchain) into TEA to effectively optimize both processes and economic parameters simultaneously. With this, this review explores the integration of data-driven technologies in the TEA framework. From literature reviews, it was found that genetic algorithm (GA) is the most applied data-driven technology in TEA, while the applications of blockchain, machine learning (ML), and artificial neural network (ANN) in TEA are still considerably scarce. Not to mention other advanced technologies, such as cyber-physical systems (CPS), IoT, cloud computing, big data analytics, digital twin (DT), and metaverse are yet to be incorporated into the existing TEA. The inclusion of set-up costs for the aforementioned technologies is also crucial for accurate TEA representation of smart industries deployment. Overall, this review serves as a reference note for future process engineers and industry stakeholders who wish to perform relevant TEA, which is capable to cover the new state-of-art elements under the new modern era.

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“…The implementation of the algorithms for the analysis will depend a lot on the types of libraries that are used by the features and functions they provide. Libraries involved: a) Matplotlib: Python standard library for graphing statistical data results [15]. b) NumPy: Besides providing mathematical functions, it contains a universal data structure that facilitates data analysis and exchanges involved in algorithms [15].…”

Section: ) Programming Language Used In the Projectmentioning

confidence: 99%

“…Libraries involved: a) Matplotlib: Python standard library for graphing statistical data results [15]. b) NumPy: Besides providing mathematical functions, it contains a universal data structure that facilitates data analysis and exchanges involved in algorithms [15]. c) Pandas: It reads a large amount of data, the data structure developed in this library leads us to manipulate the data in one and two dimensions [16].…”

Section: ) Programming Language Used In the Projectmentioning

confidence: 99%

Predictive Algorithms Analysis to Improve Sustainable Mobility

León-Granizo

Botto-Tobar

2022

JOIV : Int. J. Inform. Visualization

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The work is based on carrying out a comparative analysis of 3 prediction algorithms (Linear Regression, Neural Networks, and KNN), which allow the study of information on georeferential coordinates of moving objects, since through an exhaustive study it will be possible to know the predictions of each one. of them and then proceed to comply with the main objective that is to implement the algorithm with greater accuracy and effectiveness, making use of open Source tools that allow working with Machine Learning and thus be able to analyze the forecasts of traffic congestion that is formed in the surroundings of the University of Guayaquil, because this generates a great inconvenience for students and administrative personnel who belong to this institution and diminish an improvement in sustainable mobility. The methodology used is the Waterfall methodology, as it is a linear model of simple implementation, where each phase of the project was emphasized, allowing possible disorientation of the results to be managed and achieving the development of the proposed project without any inconvenience.

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Introduction to machine learning and Python

Cited by 24 publications

References 0 publications

Machine Learning-Based Accelerated Approaches to Infer Breakdown Pressure of Several Unconventional Rock Types

Machine Learning-Based Accelerated Approaches to Infer Breakdown Pressure of Several Unconventional Rock Types

Future era of techno-economic analysis: Insights from review

Predictive Algorithms Analysis to Improve Sustainable Mobility

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