Christ University

About: Christ University is a education organization based out in Bengaluru, India. It is known for research contribution in the topics: Computer science & Convection. The organization has 2267 authors who have published 2715 publications receiving 14575 citations. The organization is also known as: Christ College & Christ University.

Comparison of Genetic Algorithm with Particle Swarm Optimisation, Ant Colony Optimisation and Tabu Search based on University Course Scheduling System

Abstract: Objectives: Planning and allocation of the various resources according to the constraints is a hilarious task. The paper aims to find a suitable method to solve the university course scheduling problem. Methods and Statistical Analysis: This paper compares the usage of Particle Swarm Optimisation (PSO), Ant Colony Optimisation (ACO), Tabu Search and Genetic Algorithm (GA) in the preparation of University Course Scheduling System. Certain hard constraints, which has to be satisfied and some soft constraints that can be satisfied are considered. Findings: The algorithm should check for the satisfaction of the hard constraints and the possibility of satisfying the soft constraints. Application/Improvements: The performance of the suitable method is found by comparing with the other methods based on various parameters.

Bioconvection in Buoyancy Induced Flow of Williamson Nanofluid Over a Riga Plate-DTM-Padé Approach

Abstract: The buoyancy induced flow of Williamson nanofluid containing Gyrotactic microorganisms along a vertical Riga plate has been investigated. This research aims at analysing the heat and mass transfer characteristics of Williamson Nanofluid in the presence of Gyrotactic microorganisms that helps in avoiding the agglomeration of nanoparticles during the nanofluid flow. The Gyrotactic microorganisms act as active mixers that help in stabilising the nanoparticles in the suspension. Also, the movement of these cells gives rise to a macro phenomenon called bioconvection that helps in preventing the agglomeration of nanoparticles. Furthermore, the magnetic field generated due to the flow of nanofluid is considered in addition to Thermophoresis and Brownian Motion to make the results more appropriate. Buongiorno’s Model has been incorporated to frame the system of equations that govern the fluid flow. Later, lie group analysis is performed to transform these equations into ordinary differential equations that are further solved using the differential transform method with Padé approximant. It is observed that the Lorentz force generated by the Riga plate in parallel to the flow helps in increasing the velocity of the nanofluid. It is also noticed that bioconvection reduces the flow speed and enhances the heat transfer rate.

Parameter screening, optimization and artificial neural network modeling of cadmium extraction from aqueous solution using green emulsion liquid membrane

Abstract: This research was aimed to investigate the extraction of cadmium using waste cooking oil (WCO) based emulsion liquid membrane. The green emulsion liquid membrane (GELM) phase consists of waste cooking oil, a non-toxic solvent as diluent, Span 80 as surfactant, D2EHPA (Bis (2-ethylhexyl) phosphoric acid) as carrier, and hydrochloric acid solution as stripping phase. The influence of the operating parameters namely surfactant concentration (1–5v/v%), carrier concentration (2–6v/v%), agitation speed (200–600 rpm), agitation time (10–15​ min), pH of the feed solution (4–6), treat ratio (1:10–1:15), internal stripping agent (HCl) concentration (0.5–1.5 N), initial cadmium ion concentration (100–500 mg/L) and phase ratio (1:1–1:3) were studied to identify the key variables to be screened using Placket Burman design based on ‘P’ value. The significant variables were then optimized using Box–Behnken design. The stripping efficiency of cadmium was verified by varying the HCl concentration. The recycling and reuse of membrane phase was studied for 8 cycles. The mechanism of cadmium extraction by GELM was investigated. The feed forward neural network (FFNN) model having input layer composed of 5 neurons and output layer with 1 (cadmium extraction) neuron was employed to model the extraction data.