Fluid Catalytic Cracking (FCC) process is a vital unit to produce gasoline. In this research, a feed forward ANN model was developed and trained with industrial data to investigate the effect of operating variables containing reactor temperature feed flow rate, the temperature of the top of the main column and the temperature of the bottom of the debutanizer tower on quality and quantity of...

in a robust parameter design (rpd) problem, the experimenter is interested to determine the values of con-trol factors such that responses will be robust or insensitive to variability of the noise factors. response sur-face methodology (rsm) is one of the effective methods that can be employed for this purpose. since quality of products or processes is usually evaluated through several quality ...

this study focused on the optimization of al—mg to cuzn34 friction stir lap welding (fslw) process for optimal combination of rotational and traverse speeds in order to yield favorable fracture load using grey relational analysis (gra). first, the degree of freedom was calculated for the system. then, the experiments based on the target values and number of considered levels, corresponding orth...

Magnetic Optimization Algorithm (MOA) has emerged as a promising optimization algorithm that is inspired by the principles of magnetic field theory. In this paper we improve the performance of the algorithm in two aspects. First an Opposition-Based Learning (OBL) approach is proposed for the algorithm which is applied to the movement operator of the algorithm. Second, by learning from the algor...

this paper presents a methodology, based on the taguchi parameter design approach, for the optimization of process parameters for bismuth recovery from aqueous solutions. the process parameters considered are [bi3+], [nacl], temperature, and cathodic current density. in addition, cell voltage and current efficiency as two responses have been considered. an orthogonal array l9, the signal-to-noi...

A conventional Σ∆ ADC design approach is a time consuming process and needs much trials and errors. An optimization algorithm for the discrete-time single-loop Σ∆ ADCs design is proposed. Circuit nonideality models are derived in output noise power forms through a systematic circuit imperfection study. A power model is also presented in order to estimate relative power consumption. These models...

We consider parameter optimization problems which are subject to constraints given by parametrized partial differential equations (PDE). Discretizing this problem may lead to a largescale optimization problem which can hardly be solved rapidly. In order to accelerate the process of parameter optimization we will use a reduced basis surrogate model for numerical optimization. For many optimizati...

There is a strong need for sound statistical analysis of simulation and optimization algorithms. Based on this analysis, improved parameter settings can be determined. This will be referred to as tuning. Model-based investigations are common approaches in simulation and optimization. The sequential parameter optimization toolbox SPOT package for R [5] is a toolbox for tuning and understanding s...

Hyper-parameter optimization is an important problem in natural language processing (NLP) and machine learning. Recently, a group of studies has focused on using sequential Bayesian Optimization to solve this problem, which aims to reduce the number of iterations and trials required during the optimization process. In this paper, we explore this problem from a different angle, and propose a mul...