Liquid temperature prediction in bubbly flow using ant colony optimization algorithm in the fuzzy inference system as a trainer

Modelling and simulation of waste tire pyrolysis process for recovery of energy and production of valuable chemicals (BTEX)

The pyrolysis oil fraction is highly attractive for pyrolysis products. A simulated flowsheet model of a waste tire pyrolysis process is presented in this paper. A kinetic rate-based reaction model and equilibrium separation model are created in the Aspen Plus simulation package. The simulation model is effectively proven against experimental data of literature at temperatures of 400, 450, 500, 600 and 700 °C. Also, the developed model was employed to investigate the impact of temperature on the pyrolysis procedure and demonstrated that there is an optimum temperature for chain fractions. The optimum temperature to have the highest amount of limonene (as a precious chemical product of waste tire pyrolysis process) was found 500 °C. The findings indicated that the pyrolysis process is ecologically benign, although there is still space for development. In addition, a sensitivity analysis was carried out to see how altering the heating fuel in the process would affect the non-condensable gases produced in the process. Reactors and distillation columns in the Aspen Plus^® simulation model was developed to assess the technical functioning of the process (e.g., upgrading the waste tires into limonene). Furthermore, this work focuses on the optimization of the operating and structure parameters of the distillation columns in the product separation unit. The PR-BM, as well as NRTL property models, were applied in the simulation model. The calculation of non-conventional components in the model was determined using HCOALGEN and DCOALIGT property models.

Neural network compensation control of magnetic levitation ball position based on fuzzy inference

Aiming at the problem of poor transient performance of the control system caused by the control uncertainty of the undertrained neural network, a neural network compensation control method based on fuzzy inference is proposed in this paper. The method includes three control substructures: fuzzy inference block, neural network control block and basic control block. The fuzzy inference block adaptively adjusts the neural network compensation control quantity according to the control error and the error rate of change, and adds a dynamic adjustment factor to ensure the control quality at the initial stage of network learning or at the moment of signal transition. The neural network control block is composed of an identifier and a controller with the same network structure. After the identifier learns the dynamic inverse model of the controlled object online, its training parameters are dynamically copied to the controller for real-time compensation control. The basic control block uses a traditional PID controller to provide online learning samples for the neural network control block. The simulation and experimental results of the position control of the magnetic levitation ball show that the proposed method significantly reduces the overshoot and settling time of the control system without sacrificing the steady-state accuracy of neural network compensation control, and has good transient and steady-state performance and strong robustness simultaneously.

Prediction of velocity profile of water based copper nanofluid in a heated porous tube using CFD and genetic algorithm

The heat transfer improvements by simultaneous usage of the nanofluids and metallic porous foams are still an attractive research area. The Computational fluid dynamics (CFD) methods are widely used for thermal and hydrodynamic investigations of the nanofluids flow inside the porous media. Almost all studies dedicated to the accurate prediction of the CFD approach. However, there are not sufficient investigations on the CFD approach optimization. The mesh increment in the CFD approach is one of the challenging concepts especially in turbulent flows and complex geometries. This study, for the first time, introduces a type of artificial intelligence algorithm (AIA) as a supplementary tool for helping the CFD. According to the idea of this study, the CFD simulation is done for a case with low mesh density. The artificial intelligence algorithm uses learns the CFD driven data. After the intelligence achievement, the AIA could predict the fluid parameters for the infinite number of nodes or dense mesh without any limitations. So, there is no need to solve the CFD models for further nodes. This study is specifically focused on the genetic algorithm-based fuzzy inference system (GAFIS) to predict the velocity profile of the water-based copper nanofluid turbulent flow in a porous tube. The most intelligent GAFIS could perform the most accurate prediction of the velocity. Hence, the intelligence of GAFIS is tested for different values of cluster influence range (CIR), squash factor(SF), accept ratio (AR) and reject ratio (RR), the population size (PS), and the percentage of crossover (PC). The maximum coefficient of determination (~ 0.97) was related to the PS of 30, the AR of 0.6, the PC of 0.4, CIR of 0.15, the SF 1.15, and the RR of 0.05. The GAFIS prediction of the fluid velocity was in great agreement with the CFD. In the most intelligent condition, the velocity profile predicted by GAFIS was similar to the CFD. The nodes increment from 537 to 7671 was made by the GAFIS. The new predictions of the GAFIS covered all CFD results.

Prediction of gas velocity in two-phase flow using developed fuzzy logic system with differential evolution algorithm

In this investigation, differential evolution (DE) algorithm with the fuzzy inference system (FIS) are combined and the DE algorithm is employed in FIS training process. Considered data in this study were extracted from simulation of a 2D two-phase reactor in which gas was sparged from bottom of reactor, and the injected gas velocities were between 0.05 to 0.11 m/s. After doing a couple of training by making some changes in DE parameters and FIS parameters, the greatest percentage of FIS capacity was achieved. By applying the optimized model, the gas phase velocity in x direction inside the reactor was predicted when the injected gas velocity was 0.08 m/s.

Thermal prediction of turbulent forced convection of nanofluid using computational fluid dynamics coupled genetic algorithm with fuzzy interface system

Computational fluid dynamics (CFD) simulating is a useful methodology for reduction of experiments and their associated costs. Although the CFD could predict all hydro-thermal parameters of fluid flows, the connections between such parameters with each other are impossible using this approach. Machine learning by the artificial intelligence (AI) algorithm has already shown the ability to intelligently record engineering data. However, there are no studies available to deeply investigate the implicit connections between the variables resulted from the CFD. The present investigation tries to conduct cooperation between the mechanistic CFD and the artificial algorithm. The genetic algorithm is combined with the fuzzy interface system (GAFIS). Turbulent forced convection of Al₂O₃/water nanofluid in a heated tube is simulated for inlet temperatures (i.e., 305, 310, 315, and 320 K). GAFIS learns nodes coordinates of the fluid, the inlet temperatures, and turbulent kinetic energy (TKE) as inputs. The fluid temperature is learned as output. The number of inputs, population size, and the component are checked for the best intelligence. Finally, at the best intelligence, a formula is developed to make a relationship between the output (i.e. nanofluid temperatures) and inputs (the coordinates of the nodes of the nanofluid, inlet temperature, and TKE). The results revealed that the GAFIS intelligence reaches the highest level when the input number, the population size, and the exponent are 5, 30, and 3, respectively. Adding the turbulent kinetic energy as the fifth input, the regression value increases from 0.95 to 0.98. This means that by considering the turbulent kinetic energy the GAFIS reaches a higher level of intelligence by distinguishing the more difference between the learned data. The CFD and GAFIS predicted the same values of the nanofluid temperature.