Data-Based Sensing and Stochastic Analysis of Biodiesel Production Process

Ahmad, Iftikhar; Ayub, Ahsan; Ibrahim, Uzair; Khattak, Mansoor Khan; Kano, Manabu

doi:10.3390/en12010063

Cited by 13 publications

(5 citation statements)

References 31 publications

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“…Nicola et al combined GA with Aspen Plus simulation to optimize the transesterification process (Nicola et al, 2010). Another study used the data generated from Aspen Plus simulation to develop soft sensors by boosting (an ensemble machine learning (ELM) method) to predict the output material flows of transesterification simulation (Ahmad et al, 2019). Note that only 8 out of the 28 transesterification studies initially collected passed the screening process.…”

Section: Applications Of Artificial Intelligence To Bioenergy Systemsmentioning

confidence: 99%

Applications of artificial intelligence‐based modeling for bioenergy systems: A review

Ming-yang

Yao

2021

GCB Bioenergy

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Bioenergy is widely considered a sustainable alternative to fossil fuels. However, large‐scale applications of biomass‐based energy products are limited due to challenges related to feedstock variability, conversion economics, and supply chain reliability. Artificial intelligence (AI), an emerging concept, has been applied to bioenergy systems in recent decades to address those challenges. This paper reviewed 164 articles published between 2005 and 2019 that applied different AI techniques to bioenergy systems. This review focuses on identifying the unique capabilities of various AI techniques in addressing bioenergy‐related research challenges and improving the performance of bioenergy systems. Specifically, we characterized AI studies by their input variables, output variables, AI techniques, dataset size, and performance. We examined AI applications throughout the life cycle of bioenergy systems. We identified four areas in which AI has been mostly applied, including (1) the prediction of biomass properties, (2) the prediction of process performance of biomass conversion, including different conversion pathways and technologies, (3) the prediction of biofuel properties and the performance of bioenergy end‐use systems, and (4) supply chain modeling and optimization. Based on the review, AI is particularly useful in generating data that are hard to be measured directly, improving traditional models of biomass conversion and biofuel end‐uses, and overcoming the challenges of traditional computing techniques for bioenergy supply chain design and optimization. For future research, efforts are needed to develop standardized and practical procedures for selecting AI techniques and determining training data samples, to enhance data collection, documentation, and sharing across bioenergy‐related areas, and to explore the potential of AI in supporting the sustainable development of bioenergy systems from holistic perspectives.

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Section: Applications Of Artificial Intelligence To Bioenergy Systemsmentioning

confidence: 99%

Applications of artificial intelligence‐based modeling for bioenergy systems: A review

Ming-yang

Yao

2021

GCB Bioenergy

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“…Ahmad et al [90] used an ensemble learning method like Least Squares Boosting (LSBoost) integrated with the polynomial chaos expansion method (PCE) to predict quantity, quality, flow rate, the cetane number of fatty acid methyl esters (FAME), and composition in the vegetable oilbased biodiesel production process. Predicted values showed 1% uncertainty in all process parameters using mean absolute deviation percent (MADP), showing high accuracy of the proposed model in outcomes prediction and quantification uncertainty effect in the process.…”

Section: Quality Predictionmentioning

confidence: 99%

A Review on Machine Learning Application in Biodiesel Production Studies

Xing

Zheng

Sun

et al. 2021

International Journal of Chemical Engineering

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The consumption of fossil fuels has exponentially increased in recent decades, despite significant air pollution, environmental deterioration challenges, health problems, and limited resources. Biofuel can be used instead of fossil fuel due to environmental benefits and availability to produce various energy sorts like electricity, power, and heating or to sustain transportation fuels. Biodiesel production is an intricate process that requires identifying unknown nonlinear relationships between the system input and output data; therefore, accurate and swift modeling instruments like machine learning (ML) or artificial intelligence (AI) are necessary to design, handle, control, optimize, and monitor the system. Among the biodiesel production modeling methods, machine learning provides better predictions with the highest accuracy, inspired by the brain’s autolearning and self-improving capability to solve the study’s complicated questions; therefore, it is beneficial for modeling (trans) esterification processes, physicochemical properties, and monitoring biodiesel systems in real-time. Machine learning applications in the production phase include quality optimization and estimation, process conditions, and quantity. Emissions composition and temperature estimation and motor performance analysis investigate in the consumption phase. Fatty methyl acid ester stands as the output parameter, and the input parameters include oil and catalyst type, methanol-to-oil ratio, catalyst concentration, reaction time, domain, and frequency. This paper will present a review and discuss various ML technology advantages, disadvantages, and applications in biodiesel production, mainly focused on recently published articles from 2010 to 2021, to make decisions and optimize, model, control, monitor, and forecast biodiesel production.

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“…Optimized conditions were calcine temperature of 700 • C, 160 • C reaction temperature, 18 min reaction time, and 4 mmol of Zn concentration. Ahmad et al [37] used Least Squares Boosting (LSBoost) integrated with polynomial chaos expansion method in the production of vegetable oil based biodiesel under uncertainty. The average Mean Absolute Deviation Percent (MADP) values in the predicted values of the target output were 0.84 in response to 1% uncertainty in each input variable of the models.…”

Section: Quality Estimationmentioning

confidence: 99%

Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions

et al. 2021

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Machine Learning (ML) is one of the major driving forces behind the fourth industrial revolution. This study reviews the ML applications in the life cycle stages of biofuels, i.e., soil, feedstock, production, consumption, and emissions. ML applications in the soil stage were mostly used for satellite images of land to estimate the yield of biofuels or a suitability analysis of agricultural land. The existing literature have reported on the assessment of rheological properties of the feedstocks and their effect on the quality of biofuels. The ML applications in the production stage include estimation and optimization of quality, quantity, and process conditions. The fuel consumption and emissions stage include analysis of engine performance and estimation of emissions temperature and composition. This study identifies the following trends: the most dominant ML method, the stage of life cycle getting the most usage of ML, the type of data used for the development of the ML-based models, and the frequently used input and output variables for each stage. The findings of this article would be beneficial for academia and industry-related professionals involved in model development in different stages of biofuel’s life cycle.

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Data-Based Sensing and Stochastic Analysis of Biodiesel Production Process

Cited by 13 publications

References 31 publications

Applications of artificial intelligence‐based modeling for bioenergy systems: A review

Applications of artificial intelligence‐based modeling for bioenergy systems: A review

A Review on Machine Learning Application in Biodiesel Production Studies

Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions

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