Search

Industrialization increases day by day and hence the demand of Cr (VI) compounds are also increased in tanning, electroplating, printing and in dyeing industries, which has resulted in considerable amounts of waste containing Cr(VI) being emitted. Environmental scientists are quite concerned about how much Cr(VI) is present in water since it creates a severe harm to the ecosystem and humans. Because of their functional diversity, high efficacy, economic viability, and simplicity of technical operation, low-cost biosorbents made of lignocellulosic biomasses have drawn attention for elimination of Cr(VI). One versatile and efficient approach to address Cr(VI) pollution is through the use of lignocellulosic materials, for biosorption. Within this comprehensive review, we conduct an impartial evaluation of the biosorption capabilities of various lignocellulosic biosorbents, while also examining the underlying mechanisms involved in the biosorption process by focusing on crucial variables including pH, contact time, dosage, and temperature. In addition, we assemble a list of investigated lignocellulose-based biosorbents that may be beneficial. In addition to offering our recommendations for future views, the review's objective is to highlight existing trends, potential study areas, and obstacles to the practical use of the biosorption process. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Preservation of fruits by drying is one of the general and important traditional technique followed by the process industries. An accurate controller of relative humidity and temperature is required for the fruit drying control system, which determines the quality of the dried fruits. OBJECTIVES: To design optimal Propositional-Integral-Derivative (PID) controller based on the Particle Swarm Optimization and Radial Basis Functional Neural Network (PSO-RBFNN) for pineapple drying system. METHODS: A Propositional-Integral-Derivative (PID) controller based on the Particle Swarm Optimization and Radial Basis Functional Neural Network (PSO-RBFNN) was proposed in this paper for pineapple drying system. Also, the coupling relationship of relative humidity and temperature is more complicated due to the fluctuations and non-linearity in the drying system. An intelligent Adaptive Neuro Fuzzy Inference System (ANFIS) coupling model is utilized in this paper to access the coupling relationship between relative humidity and temperature. RESULTS: The proposed control system has been implemented in the MATLAB and results are compared with PID controller, Fuzzy Logic Controller (FLC) and Fuzzy PID controller for the performance constraints such as settling time, peak over shoot and steady state error. CONCLUSION: The proposed PSO-RBFNN based PID controller gives better control performance with the highly minimized settling time (42 sec for humidity and 40 sec for temperature) and completely eliminated steady state error and Peak overshoot. Finally, the PSO-RBFNN algorithm based PID controller is concluded as the effective system. [ABSTRACT FROM AUTHOR]