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275 articles for “predictive modeling”
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A Model to Study the Effect of Homogeneous Nucleation and Emulsifier Amounts on Methyl Methacrylate Emulsion Polymerization in a Seeded Semi-batch Reactor
Abstract: ABSTRACTAn extensive model is developed for methyl methacrylate emulsion polymerization. The assumption of monodispersed particle size distribution is used in developing the model. It is found that inclusion of homogeneous nucleation in the model leads to under prediction of variation of conversion with time. The effects of three different emulsifier amounts on the variation of conversion with time at 50°C isothermal reactor operation, keeping all the other ingredients constant are …
Published in International Journal of Polymer Science and Engineering · Vol. 5, Issue 2, 2019 · pp. 9–17 Read article
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PREDICTION OF RECYCLED CONCRETE AGGREGATE SELF COMPACTING CONCRETE COMPRESSIVE STRENGTH USING MATHEMATICAL MODEL
Abstract: Sustainable Development aims at improving the standard of life without compromising the environmental qualities and for future needs. These objectives can be achieved through recycling of construction/demolished waste. In Past, land filling of Construction and Demolished waste materials was the general solution. As raw Construction Material Charges have risen dramatically, we have to look for alternative solutions and methods, such as recycling of demolished waste. Recycled Aggregate Concrete not only …
Published in International Journal of Structural Engineering and Analysis · Vol. 5, Issue 2, 2019 · pp. 34–43 Read article
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A Review of AI and Machine Learning Techniques in Dew Point Pressure Prediction for Gas Condensate Systems
Abstract: Condensate gas reservoirs pose considerable management challenges due to their intricate phase behavior and the critical need for accurate predictions of dew point pressure (PDew). A precise determination of PDew is vital for optimizing production strategies, estimating reserves, and planning enhanced oil recovery operations. Traditional methods for PDew determination often involve experimental analyses that can be both costly and time-consuming, highlighting the necessity for alternative predictive models. This review consolidates …
Published in International Journal of Chemical Engineering and Processing · Vol. 11, Issue 1, 2025 Read article
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An Integrated Machine Learning and IoT-Based Framework for Predictive Urban Water Demand Management and Distribution Optimization
Abstract: Abstract Urban water resource management has become increasingly complex due to rapid urbanization, population growth, climate variability, and aging infrastructure. Conventional water management approaches are often reactive, inefficient, and incapable of handling real-time fluctuations in demand and supply. This study proposes an integrated framework that combines machine learning (ML) techniques with Internet of Things (IoT)-based real-time monitoring to enhance water demand prediction and optimize distribution systems in urban environments. The …
Published in International Journal of Water Resources Engineering · Vol. 12, Issue 1, 2026 Read article
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Soft Computing Based Prediction of Ultimate Bearing Capacity of Footings Resting on Rock Masses
Abstract: The present study attempts to predict the ultimate bearing capacity of shallow foundations on rock masses using five different soft computing techniques namely i) random forest regression approach, ii) artificial neural networks, iii) support vector machine with polykernel, iv) support vector machine with (radial basis function) RBF kernel and v) M5P model tree. The ultimate bearing capacity was assumed to be dependent rock mass rating, unconfined compressive strength of rock, …
Published in International Journal of Geological and Geotechnical Engineering · Vol. 5, Issue 2, 2019 · pp. 1–14 Read article
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Leveraging Prediction Strategies to Enhance Milk Quality and Promote Dairy Sustainability
Abstract: In the Dairy industry, ensuring high-quality milk production is vital. This research paper focuses on employing machine learning methods to predict milk quality based on diverse parameters including pH levels, temperature, fat content, and colour. By harnessing data encompassing these factors along with established quality markers, the study aims to construct a predictive model. This model seeks to anticipate variations in milk quality, providing insights into potential fluctuations in fat …
Published in International Journal of Green Chemistry · Vol. 9, Issue 2, 2023 · pp. 38–47 Read article
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Comparison of Theoretical and Experimental Investigation of Suspened Solids Sedimentation Model in Stagnant Water Environment on Crude Oil Degradation
Abstract: In this investigation the developed model of Ukpaka’s, Stoker’s and Newton’s were compared with the determine values from the experimental data obtained in the suspended solid (TS) in a stagnant water environment. Theoretical and experimental data established in this research demonstrated a good match as well as the evaluated deviation error reveals acceptable range indicating the usefulness of these models in monitoring and predicting the TS sedimentation mechanism in crude …
Published in International Journal of Chemical Separation Technology · Vol. 8, Issue 1, 2022 · pp. 1–16 Read article
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Prediction of Functional Parameters of Vegetable Stem Solid Waste Degradation Using Taylor’s Variable Model
Abstract: Mathematical model was developed to monitor, predict and simulate the functional parameters of vegetable stem solid waste degradation using Taylor’s variable model. Data obtained from the experimental research work was transformed and resolved using Laplace transformation upon the degradation of vegetable stem solid waste. The specific rate of vegetable stem solid waste, maximum specific rate and the disassociation constant rate were evaluated in terms Michaeli’s Menten and Linear method using …
Published in International Journal of Plant Biotechnology · Vol. 4, Issue 2, 2018 · pp. 30–36 Read article
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Study and Developing of Diversity Generation Methods in Heretogeneous Ensemble Models
Abstract: The article deals with the fundamental problem of generating a diversity of ensemble predictive models. Various approaches to ensemble diversity are described based on varying the input data set, varying the models used in the ensemble, varying within the model itself, and varying the way the results of the ensemble members are combined. A hybrid method for constructing a heterogeneous ensemble based on various types of neuro-fuzzy networks with a …
Published in International Journal of Distributed Computing and Technology · Vol. 7, Issue 1, 2021 · pp. 8–15 Read article
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Depression Detection System
Abstract: Traditionally, depression was identified through in-depth clinical interviews, during which the psychologist would analyze the subject’s responses to ascertain his or her mental condition. With the prevalence of depression increasing, some automated and trustworthy methods of depression recognition are needed. Our Objective it to design a model that can extract relevant features automatically as an input using efficient machine learning algorithms and further detect the depressive state of the user …
Published in International Journal of Embedded Systems and Emerging Technologies · Vol. 9, Issue 1, 2023 · pp. 28–37 Read article
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CFD Analysis and Prediction of Shockwave/Boundary-Layer Interaction at Hypersonic Flows Using RANS Models
Abstract: Interaction of shockwaves with turbulent boundary layer plays an important role in the design and operability of high-speed aerospace vehicles and air-breathing engines. The adverse pressure gradient of the shock is often strong enough to separate the boundary layer. The objective of the present research paper deals with the analysis of hypersonic viscous flows dominated by strong shockwave/boundary-layer interactions (SWBLI) over wing-flap and wing-fuselage junction configurations. The effects of the …
Published in International Journal of Computer Aided Manufacturing · Vol. 5, Issue 2, 2019 · pp. 33–50 Read article
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Prediction of Compressive Strength of Concrete with Decision Trees
Abstract: In the past few researchers predicted the compressive strength of concrete from its ingredients employing data driven technique like artificial neural network (ANN) and Decision Tree (DT). For Indian concrete records, only one study was available which had employed ANN for prediction. Evaluation of different performance metrics indicated that there was scope of improvement in the prediction performance. In this paper experimental data from the literature was utilized for development …
Published in International Journal of Concrete Technology · Vol. 2, Issue 1, 2016 · pp. 19–29 Read article
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Recent Advances in the Development & Application of the High Energy Explosives
Abstract: High energy explosives play a pivotal role in various fields ranging from military applications to industrial uses. Recent advancements in materials science, chemical engineering, and detonation physics have led to significant strides in the development and application of high energy explosives. This paper provides an overview of these recent advances, focusing on key innovations in explosive formulations, manufacturing techniques, and performance optimization. One notable area of progress is the development …
Published in International Journal of Energetic Materials · Vol. 9, Issue 2, 2023 · pp. 18–23 Read article
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Modeling Fly Ash and Alccofine Partial Replacement of Concrete Density for High Strength Development
Abstract: This paper monitor the development of density from concrete model partially replaced cement with alccofine and fly ash, the study were generated applying modeling techniques, this concept were adopted to detail the influence of other concrete properties that influenced the growth rate of concrete density generated from such model concrete. Linear influences were observed in all the figures, the study observed variations of growth rate in the study, these are …
Published in International Journal of Sustainable Building Technology · Vol. 3, Issue 1, 2020 · pp. 29–48 Read article
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An Overview on AI in Thermal Behaviour of Polymers
Abstract: The integration of Artificial Intelligence (AI) and Machine Learning (ML) into the thermalanalysis of polymers and composite materials has transitioned from experimental curiosity to acornerstone of material science. By replacing costly and time-consuming trial-and-errorlaboratory testing with predictive modeling, AI is accelerating the development of advancedthermal interface materials, fire-retardant polymers, and aerospace-grade composites. Theprediction and optimization of the thermal behavior of polymers and composite materialsrepresent a cornerstone of modern materials science, …
Published in International Journal of Composite and Constituent Materials · Vol. 12, Issue 1, 2026 · pp. 12–16 Read article
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Evaluation of Machine Learning and Deep Learning Models for Classification Tasks on Crime Data: A Comparative Study
Abstract: The classification of crime data has grown more and more important for public safety and law enforcement due to the increasing demand for precise and effective predictive models. To classify crime data, this study assesses and contrasts the effectiveness of several machine learning (ML) and deep learning (DL) models, such as decision trees, support vector machines, neural networks, and convolutional neural networks. The study evaluates the models' accuracy, efficiency, and …
Published in International Journal of Digital Communication and Analog Signals · Vol. 11, Issue 1, 2025 · pp. 1–15 Read article
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Optimization and Control of Evaporative Separation Processes
Abstract: Evaporative separation processes are widely used in various industries, including chemical manufacturing, petroleum refining, and food and beverage processing, to separate components based on their vapor pressures. These processes involve the use of equipment such as distillation columns, evaporators, and membrane-based systems, and are critical for ensuring product quality and process efficiency. However, the complexity of evaporative separation processes and the inherent variability in feedstock composition and quality make it …
Published in International Journal of Chemical Separation Technology · Vol. 8, Issue 2, 2022 · pp. 27–31 Read article
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A Customized Wireless Supervisory Infrastructure for Integration and Explicit Multiparametric MPC-Based Control of Laboratory Processes
Abstract: In this study, a custom, embedded wireless (Zigbee) supervisory infrastructure aimed at integration of laboratory processes is reported. A microcontroller based field control unit (FCU) implements closed loop control on experiments via ADAM 5000/485 data acquisition module. Data from the process (sensor/manipulation) are broadcasted from FCU using a wireless access point (WAP). A MATLAB graphic user interface (GUI) updates the data (obtained using a remote monitoring unit (RMU)) graphically. Multiparametric …
Published in International Journal of Chemical Engineering and Processing · Vol. 2, Issue 2, 2016 · pp. 10–18 Read article
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Distribution Parameter Concentration of Alkalinity and Total hardness in Stagnant Water Environment: The Concepts of Oil and Gas Emission Influence
Abstract: Model for predicting the distribution parameter concentration of alkalinity and total hardness in stagnant water environment upon the influence of oil and gasemission was investigated in this paper. The model was formulated from the basic principle of mass and momentum concept which was resolved to obtain an ordinary differential equation. A mathematical tool known as the least square method was applied to resolve the differential equation to obtain quadratic equation …
Published in International Journal of Chemical Separation Technology · Vol. 6, Issue 1, 2020 · pp. 14–22 Read article
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A Machine Learning-Based Framework for Urban Traffic Congestion Prediction and Control Using Intelligent Transportation Systems
Abstract: Abstract Traffic congestion remains a critical challenge in rapidly expanding urban areas, causing increased travel delays, excessive fuel consumption, environmental degradation, and economic inefficiencies. Traditional traffic management approaches are often insufficient, as they lack the ability to respond dynamically to continuously changing traffic conditions. This study proposes a novel data-driven framework for the prediction and management of traffic congestion using Intelligent Transportation Systems (ITS). The methodology integrates real-time and historical …
Published in International Journal of Transportation Engineering and Traffic System · Vol. 12, Issue 1, 2026 · pp. 12–17 Read article