scholarly journals Modeling of Experimentally verified Effect of Basic Input Parameters on Fuel Consumption of oil fired furnace using Machine Learning

2020 ◽  
Vol 9 (1) ◽  
pp. 2178-2181
Keyword(s):  
Machine Learning ◽  
Fuel Consumption ◽  
Multiple Regression ◽  
Process Parameters ◽  
Rotational Speed ◽  
Melting Rate ◽  
Computational Technique ◽  
Foundry Industry ◽  
Input Parameters ◽  
Basic Input

The authors have attempted to create a model, based on actual experiments conducted on an oil fired Rotary Tilting Furnace of 200 kg melting capacity installed in a foundry unit of Agra. Multiple regression machine learning has been considered as a suitable and novel tool for Modeling of basic input process parameters of rotary tilting furnace. The basic process parameters in a rotary furnace considered are RPM (rotational speed per minute), Time of melting of one charge of 200 kgs (minute) ,melting rate of furnace (kg/hr) and fuel consumed for melting of one charge need to be controlled during whole process. In this paper the relation between input parameters such as rotational speed of the furnace, time, and melting rate have been attempted to be established with output parameter fuel consumption. This model of multiple regression machine learning may found its practical applicability in foundry industry to predict the fuel consumption of furnace before putting it in actual operation and accordingly the input parameters can be controlled for desired optimal fuel consumption. The methodology consists of experimental investigations, modeling of process parameters using machine learning, followed by result analysis. The fossil fuels may not last forever and till no other alternate fuels are developed for melting the foundry industry need to optimize it. The optimized results obtained by this model and computational technique are in line with the results of actual experiments.

scholarly journals Ensemble Machine Learning Assisted Reservoir Characterization Using Field Production Data–An Offshore Field Case Study

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1052
Author(s):  
Baozhong Wang ◽  
Jyotsna Sharma ◽  
Jianhua Chen ◽  
Patricia Persaud
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Reservoir Characterization ◽  
Time Lapse ◽  
Production Data ◽  
Oil Saturation ◽  
Ensemble Machine Learning ◽  
Input Parameters ◽  
Saturation Profiles ◽  
Field Production

Estimation of fluid saturation is an important step in dynamic reservoir characterization. Machine learning techniques have been increasingly used in recent years for reservoir saturation prediction workflows. However, most of these studies require input parameters derived from cores, petrophysical logs, or seismic data, which may not always be readily available. Additionally, very few studies incorporate the production data, which is an important reflection of the dynamic reservoir properties and also typically the most frequently and reliably measured quantity throughout the life of a field. In this research, the random forest ensemble machine learning algorithm is implemented that uses the field-wide production and injection data (both measured at the surface) as the only input parameters to predict the time-lapse oil saturation profiles at well locations. The algorithm is optimized using feature selection based on feature importance score and Pearson correlation coefficient, in combination with geophysical domain-knowledge. The workflow is demonstrated using the actual field data from a structurally complex, heterogeneous, and heavily faulted offshore reservoir. The random forest model captures the trends from three and a half years of historical field production, injection, and simulated saturation data to predict future time-lapse oil saturation profiles at four deviated well locations with over 90% R-square, less than 6% Root Mean Square Error, and less than 7% Mean Absolute Percentage Error, in each case.

scholarly journals Shoulder Related Temperature Thresholds in FSSW of Aluminium Alloys

Materials ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4375
Author(s):  
David G. Andrade ◽  
Sree Sabari ◽  
Carlos Leitão ◽  
Dulce M. Rodrigues
Keyword(s):  
Heat Generation ◽  
Process Parameters ◽  
Rotational Speed ◽  
Aluminium Alloys ◽  
Spot Welding ◽  
Base Material ◽  
Main Process ◽  
Welding Temperature ◽  
Friction Stir ◽  
Tool Diameter

Friction Stir Spot Welding (FSSW) is assumed as an environment-friendly technique, suitable for the spot welding of several materials. Nevertheless, it is consensual that the temperature control during the process is not feasible, since the exact heat generation mechanisms are still unknown. In current work, the heat generation in FSSW of aluminium alloys, was assessed by producing bead-on-plate spot welds using pinless tools. Coated and uncoated tools, with varied diameters and rotational speeds, were tested. Heat treatable (AA2017, AA6082 and AA7075) and non-heat treatable (AA5083) aluminium alloys were welded to assess any possible influence of the base material properties on heat generation. A parametric analysis enabled to establish a relationship between the process parameters and the heat generation. It was found that for rotational speeds higher than 600 rpm, the main process parameter governing the heat generation is the tool diameter. For each tool diameter, a threshold in the welding temperature was identified, which is independent of the rotational speed and of the aluminium alloy being welded. It is demonstrated that, for aluminium alloys, the temperature in FSSW may be controlled using a suitable combination of rotational speed and tool dimensions. The temperature evolution with process parameters was modelled and the model predictions were found to fit satisfactorily the experimental results.

Procedure to find out the optimal ranges of process parameters for friction stir welding

2021 ◽  
pp. 146442072199314
Author(s):  
Shubham Verma ◽  
Joy Prakash Misra ◽  
Meenu Gupta
Keyword(s):  
Friction Stir Welding ◽  
Process Parameters ◽  
Tilt Angle ◽  
Welding Speed ◽  
Rotational Speed ◽  
Optical Microscope ◽  
Graphical Analysis ◽  
Friction Stir ◽  
Steepest Ascent ◽  
Vertical Milling Machine

The present study deals with the application of sequential procedure (i.e. steepest ascent) to obtain the optimum values of process parameters for conducting friction stir welding (FSW) experiments. A vertical milling machine is modified by fabricating fixture and tool ( H13 material) for performing FSW operation to join AA7039 plates. The steepest ascent technique is employed to design the experiments at different rotational speed, welding speed, and tilt angle. The ultimate tensile strength is considered as a performance characteristic for deciding the optimal levels. The mechanical and metallurgical characteristics of the joints are studied by executing tensile and microhardness tests. It is concluded from the graphical analysis of the steepest ascent technique that the optimal maximum and minimum values are 1812–1325 r/min for rotational speed, 43–26 mm/min for welding speed, and 2°–1.3° for tilt angle, respectively. Besides, optical microscope and scanning electron microscope are utilized for microstructural and fractographic analyses for a better understanding of the process.

Experimental investigations on finishing of a brass specimen by magneto-rheological honing technique

2021 ◽  
pp. 251659842110157
Author(s):  
Chinu Kumari ◽  
Sanjay Kumar Chak
Keyword(s):  
Magnetic Field ◽  
Process Parameters ◽  
Surface Finish ◽  
Rotational Speed ◽  
Field Application ◽  
Surface Finishing ◽  
Experimental Investigations ◽  
Significant Parameter ◽  
Magnetizing Current ◽  
Magneto Rheological

Magneto-rheological abrasive honing (MRAH) is an unconventional surface finishing technique that relies on abrasives mixed with a unique finishing fluid, which changes its characteristics on magnetic field application. This process imparts nanometric-level surface finish with a significant amount of uniformity. Rotating motion of the workpiece and continuous reciprocation of the finishing fluid in the MRAH process are recognized as the major aspects for adopting this process in finishing non-magnetic materials. The finishing obtained through the MRAH process relies on the workpiece’s material properties and process parameters such as concentration of abrasives in finishing fluid, rotational speed of the workpiece, and magnetic field strength/magnetizing current. To study the efficacy of MRAH process, a parametric study was conducted by performing few experiments on a brass workpiece. Design of experiment approach was adopted to plan the experiments, and the effect of different values of magnetizing current, the concentration of abrasives, and rotational speed on the surface finish were analyzed through the application of analysis of variance (ANOVA). From ANOVA, the rotational speed was found as the most significant parameter with a contribution of 48.90% on % reduction in roughness value (%∇Ra). Around 57% of roughness reduction was obtained at the optimized value of process parameters.

scholarly journals Artificial neural networks for inverse design of resonant nanophotonic components with oscillatory loss landscapes

Nanophotonics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 385-392
Author(s):  
Joeri Lenaerts ◽  
Hannah Pinson ◽  
Vincent Ginis
Keyword(s):  
Machine Learning ◽  
Optimization Procedure ◽  
Linear Mapping ◽  
Inverse Design ◽  
Design Parameters ◽  
Second Step ◽  
Input Parameters ◽  
Novel Variant ◽  
Resonant Systems ◽  
The Fourier Transform

AbstractMachine learning offers the potential to revolutionize the inverse design of complex nanophotonic components. Here, we propose a novel variant of this formalism specifically suited for the design of resonant nanophotonic components. Typically, the first step of an inverse design process based on machine learning is training a neural network to approximate the non-linear mapping from a set of input parameters to a given optical system’s features. The second step starts from the desired features, e.g. a transmission spectrum, and propagates back through the trained network to find the optimal input parameters. For resonant systems, this second step corresponds to a gradient descent in a highly oscillatory loss landscape. As a result, the algorithm often converges into a local minimum. We significantly improve this method’s efficiency by adding the Fourier transform of the desired spectrum to the optimization procedure. We demonstrate our method by retrieving the optimal design parameters for desired transmission and reflection spectra of Fabry–Pérot resonators and Bragg reflectors, two canonical optical components whose functionality is based on wave interference. Our results can be extended to the optimization of more complex nanophotonic components interacting with structured incident fields.

Prediction of Threshold Sand Rates from Acoustic Monitors Using Artificial Intelligence

2021 ◽  
Author(s):  
Ronald E. Vieira ◽  
Bohan Xu ◽  
Asad Nadeem ◽  
Ahmed Nadeem ◽  
Siamack A. Shirazi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Noise Level ◽  
Background Noise ◽  
Pipe Wall ◽  
Flow Patterns ◽  
Operating Conditions ◽  
Flow Conditions ◽  
Background Noise Level ◽  
Input Parameters

Abstract Solids production from oil and gas wells can cause excessive damage resulting in safety hazards and expensive repairs. To prevent the problems associated with sand influx, ultrasonic devices can be used to provide a warning when sand is being produced in pipelines. One of the most used methods for sand detection is utilizing commercially available acoustic sand monitors that clamp to the outside of pipe wall and measures the acoustic energy generated by sand grain impacts on the inner side of a pipe wall. Although the transducer used by acoustic monitors is especially sensitive to acoustic emissions due to particle impact, it also reacts to flow induced noise as well (background noise). The acoustic monitor output does not exceed the background noise level until a sufficient sand rate is entrained in the flow that causes a signal output that is higher than the background noise level. This sand rate is referred to as the threshold sand rate or TSR. A significant amount of data has been compiled over the years for TSR at the Tulsa University Sand Management Projects (TUSMP) for various flow conditions with stainless steel pipe material. However, to use this data to develop a model for different flow patterns, fluid properties, pipe, and sand sizes is challenging. The purpose of this work is to develop an artificial intelligence (AI) methodology using machine learning (ML) models to determine TSR for a broad range of operating conditions. More than 250 cases from previous literature as well as ongoing research have been used to train and test the ML models. The data utilized in this work has been generated mostly in a large-scale multiphase flow loop for sand sizes ranging from 25 to 300 μm varying sand concentrations and pipe diameters from 25.4 mm to 101.6 mm ID in vertical and horizontal directions downstream of elbows. The ML algorithms including elastic net, random forest, support vector machine and gradient boosting, are optimized using nested cross-validation and the model performance is evaluated by R-squared score. The machine learning models were used to predict TSR for various velocity combinations under different flow patterns with sand. The sensitivity to changes of input parameters on predicted TSR was also investigated. The method for TSR prediction based on ML algorithms trained on lab data is also validated on actual field conditions available in the literature. The AI method results reveal a good training performance and prediction for a variety of flow conditions and pipe sizes not tested before. This work provides a framework describing a novel methodology with an expanded database to utilize Artificial Intelligence to correlate the TSR with the most common production input parameters.

scholarly journals The Analysis of the Asymmetric Plate Rolling Process in the Finishing Stand 3600/ Analiza Asymetrycznego Procesu Walcowania Blach Grubych W Klatce Wykańczającej 3600

2014 ◽  
Vol 59 (4) ◽  
pp. 1533-1538
Author(s):  
A. Kawałek ◽  
H. Dyja ◽  
M. Knapinski ◽  
G. Banaszek ◽  
M. Kwapisz
Keyword(s):  
Process Parameters ◽  
Rotational Speed ◽  
Shape Factor ◽  
Rolling Process ◽  
Asymmetry Factor ◽  
Asymmetric Rolling ◽  
Rolling Mills ◽  
Plate Rolling ◽  
Strip Shape ◽  
Roll Gap

Abstract In order to enhance the quality of plates, various solutions are being implemented, including normalizing rolling, the process of rolling followed by accelerated cooling, as well as new roll gap control systems. The hydraulic positioning of rolls and the working roll bending system can be mentioned here. The implementation of those systems results in increased loads of the rolling stands and working tools, that is the rolls. Another solution aimed at enhancing the cross-sectional and longitudinal shape of rolled plate is the introduction of asymmetric rolling, which consists in the intentional change of the stress and strain state in the roll gap. Asymmetric rolling systems have been successfully implemented in strip cold rolling mills, as well as in sheet hot rolling mills. The paper present results of studies on the effect of roll rotational speed asymmetry and other rolling process parameters on the change in the shape of rolled strip and the change of rolls separating force for the conditions of normalizing rolling of plates in the finishing stand. The variable process parameters were: the roll rotational speed asymmetry factor, av; the strip shape factor, h0/D; and the relative rolling reduction, ε. Working rolls of the diameter equal to 1000 mm and a constant lower working roll rotational speed of n = 50 rpm were assumed for the tests. The asymmetric rolling process was run by varying the rotational speed of the upper roll, which was lower than that of the lower roll. The range of variation of the roll rotational speed factor, av =vd/vg, was 1.01÷1.15. A strip shape factor of h0/D = 0.05÷0.014 was assumed. The range of rolling reductions applied was ε = 0.08÷0.50. The material used for tests was steel of the S355J2G3 grade. For the simulation of the three-dimensional plastic flow of metal in the roll gap during the asymmetric hot rolling of plates, the mathematical model of the FORGE 2008 ® program was used. For the mathematical description of the effect of rolling parameters on the strip curvature and rolls separating force the special multivariable polynomial interpolation was used. This method of tensor interpolation in Borland Builder programming environment was implemented. On the basis of the carried out analysis can be state, that by using the appropriate relative rolling reduction and working roll peripheral speed asymmetry factor for a given feedstock thickness (strip shape ratio) it is possible to completely eliminate the unfavorable phenomenon of strip bending on exit from the roll gap, or to obtain the permissible strip curvature which does not obstructs the free feed of the strip to the next pass or transferring the plate to the accelerated plate cooling stations. Additionally by introducing the asymmetric plate rolling process through differentiating working roll peripheral speeds, depending on the asymmetry factor used, the magnitude of the total roll separating force can be reduced and, at the same time, a smaller elastic deflection of rolling stand elements can be achieved. As a result smaller elastic deflection of the working rolls, smaller dimensional deviations across its width and length finished plate can be obtained.

scholarly journals Fuel Consumption Prediction for Construction Trucks: A Noninvasive Approach Using Dedicated Sensors and Machine Learning

2021 ◽  
Vol 6 (11) ◽  
pp. 157
Author(s):  
Gonçalo Pereira ◽  
Manuel Parente ◽  
João Moutinho ◽  
Manuel Sampaio
Keyword(s):  
Machine Learning ◽  
Fuel Consumption ◽  
Great Variability ◽  
Accurate Estimation ◽  
Estimation Model ◽  
Digital Twins ◽  
Significant Step ◽  
The Cost ◽  
Consumption Prediction ◽  
Insight Into

Decision support and optimization tools to be used in construction often require an accurate estimation of the cost variables to maximize their benefit. Heavy machinery is traditionally one of the greatest costs to consider mainly due to fuel consumption. These typically diesel-powered machines have a great variability of fuel consumption depending on the scenario of utilization. This paper describes the creation of a framework aiming to estimate the fuel consumption of construction trucks depending on the carried load, the slope, the distance, and the pavement type. Having a more accurate estimation will increase the benefit of these optimization tools. The fuel consumption estimation model was developed using Machine Learning (ML) algorithms supported by data, which were gathered through several sensors, in a specially designed datalogger with wireless communication and opportunistic synchronization, in a real context experiment. The results demonstrated the viability of the method, providing important insight into the advantages associated with the combination of sensorization and the machine learning models in a real-world construction setting. Ultimately, this study comprises a significant step towards the achievement of IoT implementation from a Construction 4.0 viewpoint, especially when considering its potential for real-time and digital twins applications.

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