Estimation of biosurfactant yield produced by Klebseilla sp. FKOD36 bacteria using artificial neural network approach

Ahmad, Zulfiqar; Crowley, David E.; Marina, Ninoslav; Jha, Sunil Kumar

doi:10.1016/j.measurement.2015.12.019

Cited by 36 publications

(17 citation statements)

References 34 publications

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“…As summarized in Table 3, many ANN models have been applied in bioprocesses and have achieved success in the recent past (Ahmad, Crowley, Marina, & Jha, 2016; Bhattacharya, Dinesh, Dhanarajan, Sen, & Mishra, 2017; Hosseinzadeh et al, 2020; Liyanaarachchi, Nishshanka, Nimarshana, Ariyadasa, & Attalage, 2020; Pappu & Gummadi, 2016). Their remarkable feature was that the multiple related parameters were set as input variables to develop the ANN model and predict the output variables.…”

Section: Resultsmentioning

confidence: 99%

“…As shown in Table 3, compared with other models, the lower root mean square error value and higher value of R 2 (correlation coefficients) embodied the robust and accurate performance of the model developed in this study. More important, unlike the input variables in other models, such as glycerol (Pappu & Gummadi, 2016), carbon/nitrogen (Hosseinzadeh et al, 2020), and nitrogen sources (Ahmad et al, 2016), the NaOH‐related parameters were easy to access and record online, which made the prediction more convenient.…”

Section: Resultsmentioning

confidence: 99%

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A robust soft sensor to monitor 1,3‐propanediol fermentation process by Clostridium butyricum based on artificial neural network

Zhang

Zhu

Zhuang

et al. 2020

Biotech & Bioengineering

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With the aggravation of environmental pollution and energy crisis, the sustainable microbial fermentation process of converting glycerol to 1,3-propanediol (1,3-PDO) has become an attractive alternative. However, the difficulty in the online measurement of glycerol and 1,3-PDO creates a barrier to the fermentation process and then leads to the residual glycerol and therefore, its wastage. Thus, in the present study, the four-input artificial neural network (ANN) model was developed successfully to predict the concentration of glycerol, 1,3-PDO, and biomass with high accuracy. Moreover, an ANN model combined with a kinetic model was also successfully developed to simulate the fed-batch fermentation process accurately. Hence, a soft sensor from the ANN model based on NaOH-related parameters has been successfully developed which cannot only be applied in software to solve the difficulty of glycerol and 1,3-PDO online measurement during the industrialization process, but also offer insight and reference for similar fermentation processes. K E Y W O R D S 1, 3-propanediol, artificial neural network, Clostridium butyricum, soft sensor 1 | INTRODUCTION 1,3-Propanediol (1,3-PDO), an important chemical also used as a biochemical raw material, has been used for the synthesis of polymers, such as polytrimethylene terephthalate (PTT) and polytrimethylene ether glycol, and is endowed with numerous exceptional characteristics, such as good elasticity, inherent stain resistance, and low static

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

A robust soft sensor to monitor 1,3‐propanediol fermentation process by Clostridium butyricum based on artificial neural network

Zhang

Zhu

Zhuang

et al. 2020

Biotech & Bioengineering

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“…In this case, the use of advanced computational methods based on artificial intelligence can be used to predict Bs production using their dependent variables. Such is the case of a report by Ahmad et al [9], who used the Artificial Neural Network (ANN) which is used for simulating complex system performance based on limited experimental data. ANN has been shown to be a powerful tool for optimizing Bs production.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Statistical Design, a Powerful Tool for Optimizing Biosurfactant Production: A Review

Bertrand

Martínez‐Morales

Rosas-Galván

et al. 2018

Colloids and Interfaces

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Biosurfactants (Bs) have been studied for decades and applied in different industrial sectors because of their competitive biochemical characteristics, and the fact that they are environmentally friendly. Current scientific investigations mainly involve the search for novel Bs producing organisms with attractive characteristics. Bs are expected to replace synthetic surfactants in the near future, but low production yields and inefficient downstream processes have prevented their widespread use. Although there are numerous reports on Bs optimization, to date there has been no critical compilation or revision of the statistical designs and strategies employed for improved production. The purpose of this mini review is to briefly discuss the factors that affect Bs production and the importance of statistical design as an essential tool for increasing production.

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“…Too much foaming while batch processing, lower yield, availability of affordable raw materials, expenses involved with downstream processing and purification, are still some challenges in biosurfactant production at the industrial scale [ 32 – 34 ]. As a result, the success of biosurfactant production hinges on the creation of less expensive processes, particularly in the aspect of substrates, which account for 10–30% of total production costs [ 35 , 36 ]. To address this problem, processes could be linked with the use of waste as substrates which would minimize pollution while balancing overall costs [ 37 , 38 ].…”

Section: Introductionmentioning

confidence: 99%

A critical review on various feedstocks as sustainable substrates for biosurfactants production: a way towards cleaner production

et al. 2021

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The quest for a chemical surfactant substitute has been fuelled by increased environmental awareness. The benefits that biosurfactants present like biodegradability, and biocompatibility over their chemical and synthetic counterparts has contributed immensely to their popularity and use in various industries such as petrochemicals, mining, metallurgy, agrochemicals, fertilizers, beverages, cosmetics, etc. With the growing demand for biosurfactants, researchers are looking for low-cost waste materials to use them as substrates, which will lower the manufacturing costs while providing waste management services as an add-on benefit. The use of low-cost substrates will significantly reduce the cost of producing biosurfactants. This paper discusses the use of various feedstocks in the production of biosurfactants, which not only reduces the cost of waste treatment but also provides an opportunity to profit from the sale of the biosurfactant. Furthermore, it includes state-of-the-art information about employing municipal solid waste as a sustainable feedstock for biosurfactant production, which has not been simultaneously covered in many published literatures on biosurfactant production from different feedstocks. It also addresses the myriad of other issues associated with the processing of biosurfactants, as well as the methods used to address these issues and perspectives, which will move society towards cleaner production.

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Estimation of biosurfactant yield produced by Klebseilla sp. FKOD36 bacteria using artificial neural network approach

Cited by 36 publications

References 34 publications

A robust soft sensor to monitor 1,3‐propanediol fermentation process by Clostridium butyricum based on artificial neural network

A robust soft sensor to monitor 1,3‐propanediol fermentation process by Clostridium butyricum based on artificial neural network

Statistical Design, a Powerful Tool for Optimizing Biosurfactant Production: A Review

A critical review on various feedstocks as sustainable substrates for biosurfactants production: a way towards cleaner production

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