scholarly journals Machine Learning-Based Predictive Modelling of Biodiesel Production—A Comparative Perspective

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1122
Author(s):  
Krishna Kumar Gupta ◽  
Kanak Kalita ◽  
Ranjan Kumar Ghadai ◽  
Manickam Ramachandran ◽  
Xiao-Zhi Gao
Keyword(s):  
Machine Learning ◽  
Fatty Acid ◽  
Free Fatty Acid ◽  
Random Forest ◽  
Production Process ◽  
Process Parameters ◽  
Predictive Modelling ◽  
Biodiesel Production ◽  
Random Forest Regression ◽  
Physical Experiments

Owing to the ever-growing impetus towards the development of eco-friendly and low carbon footprint energy solutions, biodiesel production and usage have been the subject of tremendous research efforts. The biodiesel production process is driven by several process parameters, which must be maintained at optimum levels to ensure high productivity. Since biodiesel productivity and quality are also dependent on the various raw materials involved in transesterification, physical experiments are necessary to make any estimation regarding them. However, a brute force approach of carrying out physical experiments until the optimal process parameters have been achieved will not succeed, due to a large number of process parameters and the underlying non-linear relation between the process parameters and responses. In this regard, a machine learning-based prediction approach is used in this paper to quantify the response features of the biodiesel production process as a function of the process parameters. Three powerful machine learning algorithms—linear regression, random forest regression and AdaBoost regression are comprehensively studied in this work. Furthermore, two separate examples—one involving biodiesel yield, the other regarding biodiesel free fatty acid conversion percentage—are illustrated. It is seen that both random forest regression and AdaBoost regression can achieve high accuracy in predictive modelling of biodiesel yield and free fatty acid conversion percentage. However, AdaBoost may be a more suitable approach for biodiesel production modelling, as it achieves the best accuracy amongst the tested algorithms. Moreover, AdaBoost can be more quickly deployed, as it was seen to be insensitive to number of regressors used.

scholarly journals A Comparative Study of Linear, Random Forest and AdaBoost Regressions for Modeling Non-Traditional Machining

Processes ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 2015
Author(s):  
G. Shanmugasundar ◽  
M. Vanitha ◽  
Robert Čep ◽  
Vikas Kumar ◽  
Kanak Kalita ◽  
...  
Keyword(s):  
Random Forest ◽  
Linear Regression ◽  
Process Parameters ◽  
Removal Rate ◽  
Predictive Modelling ◽  
Random Forest Regression ◽  
Electro Discharge Machining ◽  
Functional Knowledge ◽  
Lower Material ◽  
Significant Attention

Non-traditional machining (NTM) has gained significant attention in the last decade due to its ability to machine conventionally hard-to-machine materials. However, NTMs suffer from several disadvantages such as higher initial cost, lower material removal rate, more power consumption, etc. NTMs involve several process parameters, the appropriate tweaking of which is necessary to obtain economical and suitable results. However, the costly and time-consuming nature of the NTMs makes it a tedious and expensive task to manually investigate the appropriate process parameters. The NTM process parameters and responses are often not linearly related and thus, conventional statistical tools might not be enough to derive functional knowledge. Thus, in this paper, three popular machine learning (ML) methods (viz. linear regression, random forest regression and AdaBoost regression) are employed to develop predictive models for NTM processes. By considering two high-fidelity datasets from the literature on electro-discharge machining and wire electro-discharge machining, case studies are shown in the paper for the effectiveness of the ML methods. Linear regression is observed to be insufficient in accurately mapping the complex relationship between the process parameters and responses. Both random forest regression and AdaBoost regression are found to be suitable for predictive modelling of NTMs. However, AdaBoost regression is recommended as it is found to be insensitive to the number of regressors and thus is more readily deployable.

scholarly journals A novel framework for designing a multi-DoF prosthetic wrist control using machine learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chinmay P. Swami ◽  
Nicholas Lenhard ◽  
Jiyeon Kang
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Upper Limb ◽  
Daily Living ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Random Forest Regression ◽  
Prosthetic Devices ◽  
Upper Limb Function ◽  
The Neural Network

AbstractProsthetic arms can significantly increase the upper limb function of individuals with upper limb loss, however despite the development of various multi-DoF prosthetic arms the rate of prosthesis abandonment is still high. One of the major challenges is to design a multi-DoF controller that has high precision, robustness, and intuitiveness for daily use. The present study demonstrates a novel framework for developing a controller leveraging machine learning algorithms and movement synergies to implement natural control of a 2-DoF prosthetic wrist for activities of daily living (ADL). The data was collected during ADL tasks of ten individuals with a wrist brace emulating the absence of wrist function. Using this data, the neural network classifies the movement and then random forest regression computes the desired velocity of the prosthetic wrist. The models were trained/tested with ADLs where their robustness was tested using cross-validation and holdout data sets. The proposed framework demonstrated high accuracy (F-1 score of 99% for the classifier and Pearson’s correlation of 0.98 for the regression). Additionally, the interpretable nature of random forest regression was used to verify the targeted movement synergies. The present work provides a novel and effective framework to develop an intuitive control for multi-DoF prosthetic devices.

scholarly journals Application of random forest regression to the calculation of gas-phase chemistry within the GEOS-Chem chemistry model v10

2019 ◽  
Vol 12 (3) ◽  
pp. 1209-1225 ◽  
Author(s):  
Christoph A. Keller ◽  
Mat J. Evans
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Gas Phase ◽  
Atmospheric Chemistry ◽  
Random Forest Regression ◽  
Data Set ◽  
Gas Phase Chemistry ◽  
Chemical Conditions ◽  
Phase Chemistry ◽  
The Impact

Abstract. Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models are numerically intense, and previous attempts to reduce the numerical cost of chemistry solvers have not delivered transformative change. We show here the potential of a machine learning (in this case random forest regression) replacement for the gas-phase chemistry in atmospheric chemistry transport models. Our training data consist of 1 month (July 2013) of output of chemical conditions together with the model physical state, produced from the GEOS-Chem chemistry model v10. From this data set we train random forest regression models to predict the concentration of each transported species after the integrator, based on the physical and chemical conditions before the integrator. The choice of prediction type has a strong impact on the skill of the regression model. We find best results from predicting the change in concentration for long-lived species and the absolute concentration for short-lived species. We also find improvements from a simple implementation of chemical families (NOx = NO + NO2). We then implement the trained random forest predictors back into GEOS-Chem to replace the numerical integrator. The machine-learning-driven GEOS-Chem model compares well to the standard simulation. For ozone (O3), errors from using the random forests (compared to the reference simulation) grow slowly and after 5 days the normalized mean bias (NMB), root mean square error (RMSE) and R2 are 4.2 %, 35 % and 0.9, respectively; after 30 days the errors increase to 13 %, 67 % and 0.75, respectively. The biases become largest in remote areas such as the tropical Pacific where errors in the chemistry can accumulate with little balancing influence from emissions or deposition. Over polluted regions the model error is less than 10 % and has significant fidelity in following the time series of the full model. Modelled NOx shows similar features, with the most significant errors occurring in remote locations far from recent emissions. For other species such as inorganic bromine species and short-lived nitrogen species, errors become large, with NMB, RMSE and R2 reaching >2100 % >400 % and <0.1, respectively. This proof-of-concept implementation takes 1.8 times more time than the direct integration of the differential equations, but optimization and software engineering should allow substantial increases in speed. We discuss potential improvements in the implementation, some of its advantages from both a software and hardware perspective, its limitations, and its applicability to operational air quality activities.

scholarly journals The Effect of Co-solvent on Esterification of Oleic Acid Using Amberlyst 15 as Solid Acid Catalyst in Biodiesel Production

2018 ◽  
Vol 156 ◽  
pp. 03002
Author(s):  
Iwan Ridwan ◽  
Mukhtar Ghazali ◽  
Adi Kusmayadi ◽  
Resza Diwansyah Putra ◽  
Nina Marlina ◽  
...  
Keyword(s):  
Oleic Acid ◽  
Fatty Acid ◽  
Free Fatty Acid ◽  
Solid Acid ◽  
Solid Acid Catalyst ◽  
Acid Catalyst ◽  
Biodiesel Production ◽  
Molar Ratio ◽  
Amberlyst 15 ◽  
Acid Esterification

The oleic acid solubility in methanol is low due to two phase separation, and this causes a slow reaction time in biodiesel production. Tetrahydrofuran as co-solvent can decrease the interfacial surface tension between methanol and oleic acid. The objective of this study was to investigate the effect of co-solvent, methanol to oleic acid molar ratio, catalyst amount, and temperature of the reaction to the free fatty acid conversion. Oleic acid esterification was conducted by mixing oleic acid, methanol, tetrahydrofuran and Amberlyst 15 as a solid acid catalyst in a batch reactor. The Amberlyst 15 used had an exchange capacity of 2.57 meq/g. Significant free fatty acid conversion increments occur on biodiesel production using co-solvent compared without co-solvent. The highest free fatty acid conversion was obtained over methanol to the oleic acid molar ratio of 25:1, catalyst use of 10%, the co-solvent concentration of 8%, and a reaction temperature of 60°C. The highest FFA conversion was found at 28.6 %, and the steady state was reached after 60 minutes. In addition, the use of Amberlyst 15 oleic acid esterification shows an excellent performance as a solid acid catalyst. Catalytic activity was maintained after 4 times repeated use and reduced slightly in the fifth use.

Biodiesel Production from the High Free Fatty Acid “Hevea brasiliensis” and Fuel Properties Characterization

2014 ◽  
Vol 625 ◽  
pp. 897-900 ◽  
Author(s):  
Junaid Ahmad ◽  
Suzana Yusup ◽  
Awais Bokhari ◽  
Ruzaimah Nik Mohammad Kamil
Keyword(s):  
Fatty Acid ◽  
Free Fatty Acid ◽  
Seed Oil ◽  
Standard Procedure ◽  
Fuel Properties ◽  
Biodiesel Production ◽  
Modern World ◽  
High Free Fatty Acid ◽  
Rubber Seed Oil ◽  
Rubber Seed

Energy crises, depletion of fossil fuel reservoirs, environmental pollution, global warming, green house effect and starvation are becoming very serious problems in the modern world. Biodiesel is a liquid fuel which can be the best alternative for the fossil fuels. In this study, non-edible rubber seed oil (RSO) with high free fatty acid (FFA) content (45%) was used for the production of biodiesel. The process comprises of two steps, in the first step acid esterification was used to reduce the FFA and in the second step base transesterification was employed to convert the treated oil into rubber seed oil methyl esters (RSOMEs). The conversion yield of biodiesel was analyzed using gas chromatography. The fuel properties were tested using the standard procedure of ASTM D6751 and EN14214. All the properties were within the ranges of the biodiesel standards. The result shows that rubber seed oil is a potential non-edible source for biodiesel production.

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