Improved deinking and biobleaching efficiency of enzyme consortium from Thermomyces lanuginosus VAPS25 using genetic Algorithm-Artificial neural network based tools

Regression modelling strategies for projected and sustainable kraft pulping of wheat straw

The demand for paper and paper-based packaging has seen a massive increase in past years, resulting in accelerated deforestation to meet the rising demand, negatively impacting the environment, and there is a need to look towards different non-woody raw materials. Kraft pulping (KP) is widely used in paper making, for which the chemical dose, temperature, time, and energy required must be optimized, for which many insignificant experimental trials are performed. An effort is made to solve this problem by developing the regression equations with the help of Excel using One Factor at a Time Analysis (OFAT), followed by carrying out design of experiments (DoE) using orthogonal approach and regression analysis in Minitab software. Life cycle Assessment (LCA) using the Open-LCA software estimates the effect of chemicals and energy required during pulping on human health, ecosystem quality, and resource depletion. Using regression analysis, the equations for predicting kappa number, yield (%), total energy consumed, and mechanical properties of the paper sheet showed a good fit with an R2 value in the range of 0.90-0.99. Apart from that, the mechanical properties, namely tensile index (41.43 Nm/g), tear index (6.96 mN m2/g), bending stiffness (0.5 mN m), and burst index (3.92 kPa m2/g) of the unbeaten sheet, were determined experimentally at optimized conditions. Based on the Open-LCA result, the optimized pulping conditions had less impact on human health, ecosystem quality, and resource depletion. Industries can use the model to predict the values of kappa number, yield, mechanical properties, and energy consumption without performing optimization experiments that may impact the industry's economy to a greater extent.

Application of ANN-MOGA for nutrient sequestration for wastewater remediation and production of polyunsaturated fatty acid (PUFA) by Chlorella sorokiniana MSP1

Chlorella bears excellent potential in removing nutrients from industrial wastewater and lipid production enriched with polyunsaturated fatty acids. However, due to the changing nutrient dynamics of wastewater, growth and metabolic activity of Chlorella are affected. In order to sustain microalgal growth in wastewater with concomitant production of PUFA rich lipids, RSM (Response Surface Methodology) followed by heuristic hybrid computation model ANN-MOGA (Artificial Neural Network- Multi-Objective Genetic Algorithm) were implemented. Preliminary experiments conducted taking one factor at a time and design matrix of RSM with process variables viz. Sodium chloride (1 mM-40 mM), Magnesium sulphate (100 mg-800 mg) and incubation time (4th day to 20th day) were validated by ANN-MOGA. The study reported improved biomass and lipid yield by 54.25% and 12.76%, along with total nitrogen and phosphorus removal by 21.92% and 18.72% respectively using ANN-MOGA. It was evident from FAME results that there was a significantly improved concentration of linoleic acid (19.1%) and γ-linolenic acid (21.1%). Improved PUFA content makes it a potential feedstock with application in cosmeceutical, pharmaceutical and nutraceutical industry. The study further proves that C. sorokiniana MSP1 mediated industrial wastewater treatment with PUFA production is an effective way in providing environmental benefits along with value addition. Moreover, ANN-MOGA is a relevant tool that could control microalgal growth in wastewater.

Artificial Intelligence Aided Lipase Production and Engineering for Enzymatic Performance Improvement

With the development of artificial intelligence (AI), tailoring methods for enzyme engineering have been widely expanded. Additional protocols based on optimized network models have been used to predict and optimize lipase production as well as properties, namely, catalytic activity, stability, and substrate specificity. Here, different network models and algorithms for the prediction and reforming of lipase, focusing on its modification methods and cases based on AI, are reviewed in terms of both their advantages and disadvantages. Different neural networks coupled with various algorithms are usually applied to predict the maximum yield of lipase by optimizing the external cultivations for lipase production, while one part is used to predict the molecule variations affecting the properties of lipase. However, few studies have directly utilized AI to engineer lipase by affecting the structure of the enzyme, and a set of research gaps needs to be explored. Additionally, future perspectives of AI application in enzymes, including lipase engineering, are deduced to help the redesign of enzymes and the reform of new functional biocatalysts. This review provides a new horizon for developing effective and innovative AI tools for lipase production and engineering and facilitating lipase applications in the food industry and biomass conversion.

Analysis of endoglucanases production using metatranscriptomics and proteomics approach

The cellulases are among the most used enzyme in industries for various purposes. They add up to the green economy perspective and cost-effective production of enterprises. Biorefineries, paper industries, and textile industries are foremost in their usage. The production of endoglucanases from microorganisms is a valuable resource and can be exploited with the help of biotechnology. The present review provides some insight into the uses of endoglucanases in different industries and the potent fungal source of these enzymes. The advances in the enzyme technology has helped towards understanding some pathways to increase the production of industrial enzymes from microorganisms. The proteomics analysis and systems biology tools also help to identify these pathways for the enhanced production of such enzymes. This review deciphers the use of proteomics tools to analyze the potent microorganisms and identify suitable culture conditions to increase the output of endoglucanases. The review also includes the role of quantitative proteomics which is a powerful technique to get results faster and more timely. The role of metatranscriptomic approaches are also described which are helpful in the enzyme engineering for their efficient use under industrial conditions. Conclusively, this review helps to understand the challenges faced in the industrial use of endoglucanases and their further improvement.

Artificial intelligence technologies in bioprocess: Opportunities and challenges

Bioprocess control and optimization are crucial for tapping the metabolic potential of microorganisms, and which have made great progress in the past decades. Combination of the current control and optimization technologies with the latest computer-based strategies will be a worth expecting way to improve bioprocess further. Recently, artificial intelligence (AI) emerged as a data-driven technique independent of the complex interactions used in mathematical models and has been gradually applied in bioprocess. In this review, firstly, AI-guided modeling approaches of bioprocess are discussed, which are widely applied to optimize critical process parameters (CPPs). Then, AI-assisted rapid detection and monitoring technologies employed in bioprocess are summarized. Next, control strategies according to the above two technologies in bioprocess are analyzed. Lastly, current research gaps and future perspectives on AI-guided optimization and control technologies are discussed. This review provides theoretical guidance for developing AI-guided bioprocess optimization and control technologies.