Optimized Production of Biodiesel Using Internet of Things Sensed Temperature with Hybrid Particle Swarm Optimization

Biofuel production offers a non-fossil fuel that can be utilized in modern engines without any redesign. Regardless of receiving rising attention, many researchers have explored microalgae-based biofuel production and found biodiesel production is cost-effective compared to petroleum-centered conventional fuels. The primary reason is that the lipid accumulation of microalgae is possible. An efficient technique is proposed for optimized biodiesel manufacturing with microalgae through an IoT device with the hybrid particle swarm optimization (HPSO) algorithm for elapsing such drawbacks. First, the component of biodiesel is determined. Then, from the components, the temperature value is sensed through the IoT device. Based on the obtained temperature, the reaction parameters are optimized with HPSO to increase productivity and reduce cost. Finally, we observed performance and comparative analysis. The experimental results contrasted with the existent particle swarm optimization (PSO) and genetic algorithm (GA) concerning iteration’s temperature, concentration, production, and fitness. The present HPSO algorithm has differed from the existing PSO and GA concerning IoT sensed temperature and production function. Fitness value and instance concentration are the performance parameters. It varies based on the iteration values. Thus, the proposed optimized biodiesel production is advanced when weighed down with the top-notch methods.

HylleraasMD: A Domain Decomposition-Based Hybrid Particle-Field Software for Multi-Scale Simulations of Soft Matter

We present HylleraasMD (HyMD), a comprehensive implementation of the recently proposed Hamiltonian formulation of hybrid particle-field molecular dynamics (hPF). The methodology is based on tunable, grid-independent length-scale of coarse graining, obtained by filtering particle densities in reciprocal space. This enables systematic convergence of energies and forces by grid refinement, also eliminating non-physical force aliasing. Separating the time integration of fast modes associated with internal molecular motion, from slow modes associated with their density fields, we implement the first time-reversible hPF simulations. HyMD comprises the optional use of explicit electrostatics, which, in this formalism, corresponds to the long-range potential in Particle-Mesh Ewald. We demonstrate the ability of HhPF to perform simulations in the microcanonical and canonical ensembles with a series of test cases, comprising lipid bilayers and vesicles, surfactant micelles, and polypeptide chains, comparing our results to established literature. An on-the-fly increase of the characteristic coarse graining length significantly speeds up dynamics, accelerating self-diffusion and leading to expedited aggregation. Exploiting this acceleration, we find that the time scales involved in the self-assembly of polymeric structures can lie in the tens to hundreds of picoseconds instead of the multi microsecond regime observed with comparable coarse-grained models.

An Energy Efficient Trust Based Routing Scheme Using Hybrid Particle Swarm Optimization for Wireless Sensor Based Healthcare Networks

Healthcare networks are so sensitive and requires faster yet reliable data transmission. The problem based on congestion degrades the resources that lead to the failure of sensor nodes and faulty node misbehavior. In addition to this, increased energy computation, network performance minimizes the network lifetime. So to overcome such drawbacks, this paper proposes trust-based congestion aware using Hybrid Particle Swarm Optimization (HPSO) in Wireless Sensor based Healthcare Networks (WSHN). The proposed approach comprises two significant phases. The initial phase involves the calculation of congestion state among various nodes and the of trust values. Thus an optimal congestion metric is obtained. In the second phase, two diverse metrics namely distance and trust congestion metrics are executed using HPSO algorithm for optimal data packet routing from the base stations to the source node. This article presents a novel HPSO algorithm that utilises two distinct operators, namely the emigration and immigration processes, as well as the mutation process of the Bio-geographical based Optimization (BBO) algorithm, for presenting the optimal data routing protocol. The experimental outcomes and comparison analysis demonstrate that the proposed strategy outperforms several alternative approaches.

Electric vehicle charging station planning with dynamic prediction of elastic charging demand: a hybrid particle swarm optimization algorithm

This paper is concerned with the electric vehicle (EV) charging station planning problem based on the dynamic charging demand. Considering the dynamic charging behavior of EV users, a dynamic prediction method of EV charging demand is proposed by analyzing EV users’ travel law via the trip chain approach. In addition, a multi-objective charging station planing problem is formulated to achieve three objectives: (1) maximize the captured charging demands; (2) minimize the total cost of electricity and the time consumed for charging; and (3) minimize the load variance of the power grid. To solve such a problem, a novel method is proposed by combining the hybrid particle swarm optimization (HPSO) algorithm with the entropy-based technique for order preference by similarity to ideal solution (ETOPSIS) method. Specifically, the HPSO algorithm is used to obtain the Pareto solutions, and the ETOPSIS method is employed to determine the optimal scheme. Based on the proposed method, the siting and sizing of the EV charging station can be planned in an optimal way. Finally, the effectiveness of the proposed method is verified via the case study based on a test system composed of an IEEE 33-node distribution system and a 33-node traffic network system.