Microwave-assisted chemical pre-treatment of waste sorghum leaves: Process optimization and development of an intelligent model for determination of volatile compound fractions

Rorke, Daneal C.S.; Suinyuy, Terence N.; Kana, E.B. Gueguim

doi:10.1016/j.biortech.2016.10.048

Cited by 28 publications

(6 citation statements)

References 31 publications

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“…In a study by Rorke et al 49 . an ensemble ANN model was developed to predict the relative fractions of 21 volatile compounds generated during the pre‐treatment of sorghum leaves using a microwave‐assisted chemical method.…”

Section: Application In Pre‐treatment Process Modelingmentioning

confidence: 99%

“…This indicated that employing ANN for optimization might increase the process's economic feasibility even further. In a study by Rorke et al 49 an ensemble ANN model was developed to predict the relative fractions of 21 volatile compounds generated during the pre-treatment of sorghum leaves using a microwave-assisted chemical method. The neural network consists of a committee of three multilayer perceptron (MLP) ANN models.…”

Section: Application In Pre-treatment Process Modelingmentioning

confidence: 99%

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Artificial neural networks in valorization process modeling of lignocellulosic biomass

Pradhan

Jaiswal

2022

Biofuels Bioprod Bioref

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Value-added products such as biofuels, chemicals, enzymes, and many others can be prepared from lignocellulosic biomass (LCB). To achieve high yields of these value-added products, powerful tools such as artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) can be utilized during process development. In this article, we have therefore reviewed the recent application of ANN and ANFIS in modeling LCB valorization processes. Studies have shown the high predictive capability of both ANN and ANFIS for a range of different processes such as pre-treatment processes (microwave-assisted, organosolv-, ultrasound-assisted pre-treatment and many others), thermal processes (pyrolysis and gasification), enzymatic hydrolysis, and fermentation processes. These tools have also shown outstanding accuracy in predicting elemental composition and thermal characteristics of biomass by using only the proximate composition of LCB as the input information. In combination with evolutionary algorithms like genetic algorithm, particle swarm optimization or ant colony optimization, the ANN and ANFIS tools have shown excellent results in obtaining operational conditions for the efficient production of bioethanol, biogas, organic acids, lignin, and enzymes. However, there are only limited reports of the application of ANN and ANFIS in enzyme, organic acid and lignin production. Further research is therefore required to assess the suitability of using these tools in process development for the production of lignin, enzymes, and organic acids.

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Section: Application In Pre‐treatment Process Modelingmentioning

confidence: 99%

Section: Application In Pre-treatment Process Modelingmentioning

confidence: 99%

Artificial neural networks in valorization process modeling of lignocellulosic biomass

Pradhan

Jaiswal

2022

Biofuels Bioprod Bioref

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show abstract

“…Some works where combined pretreatments with MHP were used to produce high added-value products in terms of biorefinery are mentioned: Rorke et al [139] obtained 21 volatile compounds from waste sorghum leaves pretreated with microwave assisted with 2.5% HCl pretreatment They found aliphatic acids, [140] studied the production of HMF and furfural (building block among the top 10 bio-based chemicals) from corn stalk, rice straw and pine wood treated by ionic liquid in the presence of CrCl 3 under microwave irradiation (650 W for 3 min), results showed HMF and furfural yields of 45-52% and 23-31%, respectively. Lacerda et al [141] studied the production of HMF and furfural from carnauba palm laves, macauba pulp and shell and pine nut shell.…”

Section: Microwave Heating As Pretreatment Of Lignocellulosic Materialsmentioning

confidence: 99%

Microwave heating processing as alternative of pretreatment in second-generation biorefinery: An overview

Aguilar-Reynosa

Romaní

Rodríguez-Jasso

et al. 2017

Energy Conversion and Management

283

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The development of a feasible biorefinery is in need of alternative technologies to improve lignocellulosic biomass conversion by the suitable use of energy. Microwave heating processing (MHP) is emerging as promising unconventional pretreatment of lignocellulosic materials (LCMs). MHP applied as pretreatment induces LCMs breakdown through the molecular collision caused by the dielectric polarization. Polar particles movement generates a quick heating consequently the temperatures and times of process are lower. In this way, MHP has positioned as green technology in comparison with other types of heating. Microwave technology represents an excellent option to obtain susceptible substrates to enzymatic saccharification and subsequently in the production of bioethanol and high-added compounds. However, it is still necessary to study the dielectric properties of materials, and conduct economic studies to achieve development in pilot and industrial scale. This work aims to provide an overview of recent progress and alternative configurations for combining the application of microwave technology on the pretreatment of LCMs in terms of biorefinery.

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“…The field of machine learning is based on designing software algorithms that learn from data, discern patterns, and make predictions . This powerful data-driven approach is becoming increasingly popular in many fields including medicine, material science, energy, and engineering, − because of the increasing availability and improvements in machine learning tools and datasets . While still not used extensively in the field of catalysis and sustainability, groups are starting to use machine learning algorithms such as artificial neural networks to help predict catalyst performance. − In addition, it is becoming increasingly popular to use a combination of density functional theory (DFT) calculations and machine learning algorithms to accelerate the search for new catalysts. ,− …”

Section: Introductionmentioning

confidence: 99%

Evaluation of Machine Learning Models on Electrochemical CO₂ Reduction Using Human Curated Datasets

Farris

Niang-Trost

Branicky

et al. 2022

ACS Sustainable Chem. Eng.

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Machine learning holds the potential to be a powerful tool to aid in designing catalytic and sustainable chemical systems. However, it is important for experimental researchers to understand the capabilities of different machine learning models when trained on experimental data. In this work, we trained three different machine learning algorithms (decision tree, random forest, and multilayer perceptron) with a hand-curated dataset of 127 reaction conditions for electrocatalytic CO 2 reduction on heterogeneous catalysts in aqueous electrolytes. The input to the machine learning models were the experimental conditions, and we posed four separate outputs to each of these machine learning algorithms: (1) if the number of proton-coupled electron transfer events was greater than two, (2) if carbon−carbon coupling occurred, (3) if ethylene was the major product, and (4) major product prediction. We observed that with a dataset of this size, all three machine learning models could achieve accuracies between 0.7 and 0.8 for the three binary classification problems (1, 2, and 3). Also, the shallow learning decision tree and random forest models performed equal to or better than the deep learning multilayer perceptron models. In the multiclass classification problem (i.e., predicting the product) the accuracy for all models decreased, with the random forest model producing the highest accuracy of 0.6. Analysis of the models showed that machine learning can independently arrive at conclusions that are well-known in the literature, e.g., that Cu is an important catalyst for producing high-carbon content products, and discern more-complicated patterns, with respect to feature importance.

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Microwave-assisted chemical pre-treatment of waste sorghum leaves: Process optimization and development of an intelligent model for determination of volatile compound fractions

Cited by 28 publications

References 31 publications

Artificial neural networks in valorization process modeling of lignocellulosic biomass

Artificial neural networks in valorization process modeling of lignocellulosic biomass

Microwave heating processing as alternative of pretreatment in second-generation biorefinery: An overview

Evaluation of Machine Learning Models on Electrochemical CO₂ Reduction Using Human Curated Datasets

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Microwave-assisted chemical pre-treatment of waste sorghum leaves: Process optimization and development of an intelligent model for determination of volatile compound fractions

Cited by 28 publications

References 31 publications

Artificial neural networks in valorization process modeling of lignocellulosic biomass

Artificial neural networks in valorization process modeling of lignocellulosic biomass

Microwave heating processing as alternative of pretreatment in second-generation biorefinery: An overview

Evaluation of Machine Learning Models on Electrochemical CO2 Reduction Using Human Curated Datasets

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Evaluation of Machine Learning Models on Electrochemical CO₂ Reduction Using Human Curated Datasets