No abstract

The most abundant biopolymer accessible on earth is lignocellulose. The hydrolysis of lignocelluloses is completed through enzyme groups such as ligninases, hemicelluloses, and cellulases as independently and together called lignocellulolytic enzymes. This review is focused on dealing with enhanced production of cellulose using modern techniques like strain improvement, consolidated bioprocessing, metabolic engineering, and recombinant technology, and defines the technology to boost the enzyme activity and its yield. Furthermore, the restrains linked with the production of cellulolytic enzyme and the direction of upcoming research to offer a complete strategy to improve cellulase yield with novel properties for industrial application at cost‐effective levels are mentioned.Practical applicationsCellulase is a complex enzyme consisting of an endoglucanase, exoglucanase, and β‐D‐glucosidase which acts in combination to release a small unit of glucose. Cellulase has vast potential in the industrial sector especially in the food and beverage as well as paper and pulp industries. The parameters such as strain improvement and heterologous gene expression are involved to enhance the cellulase production. This review discusses the cost‐effective production of cellulolytic enzyme and also emphasizes the limitations and future prospects of this enzyme production on a large scale.

The scientific fields’ deals with day to day problems, like economic planning and engineering design, mostly are disconnected, high dimensional, multimodal and oscillated optimization problems. These complex problems cannot be solved well within reasonable time using conventional process based on gradient. The natural phenomenon motivates and animal group behavior characteristics, the investigators have anticipated many efficient natural heuristic algorithms for high-dimensional complex optimization problems in real-world. This manuscript deals with one of the meta-heuristic algorithm, Whale Swarm Optimization algorithm (WSO) and compares the performance with RMS prop optimization techniques.

No abstract

No abstract