Computer modeling of high-pressure leaching process of nickel laterite by design of experiments and neural networks

Due to the complex chemical composition of nickel ores, the requests for the decrease of production costs, and the increase of nickel extraction in the existing depletion of high-grade sulfide ores around the world, computer modeling of nickel ore leaching process be- came a need and a challenge. In this paper, the design of experiments （DOE） theory was used to determine the optimal experimental design plan matrix based on the D optimality criterion. In the high-pressure sulfuric acid leaching （HPSAL） process for nickel laterite in ＂Rudjinci＂ ore in Serbia, the temperature, the sulfuric acid to ore ratio, the stirring speed, and the leaching time as the predictor variables, and the degree of nickel extraction as the response have been considered. To model the process, the multiple linear regression （MLR） and response surface method （RSM）, together with the two-level and four-factor full factorial central composite design （CCD） plan, were used. The proposed re- gression models have not been proven adequate. Therefore, the artificial neural network （ANN） approach with the same experimental plan was used in order to reduce operational costs, give a better modeling accuracy, and provide a more successful process optimization. The model is based on the multi-layer neural networks with the back-propagation （BP） learning algorithm and the bipolar sigmoid activation function.

Design of experiments unravels insights into selective ethylene or methane production on evaporated Cu catalysts

As a highly tempting technology to close the carbon cycle,electrochemical CO_(2)reduction calls for the development of highly efficient and durable electrocatalysts.In the current study,Design of Experiments utilizing the response surface method is exploited to predict the optimal process variables for preparing high-performance Cu catalysts,unraveling that the selectivity towards methane or ethylene can be simply modulated by varying the evaporation parameters,among which the Cu film thickness is the most pivotal factor to determine the product selectivity.The predicted optimal catalyst with a low Cu thickness affords a high methane Faradaic efficiency of 70.6%at the partial current density of 211.8 m A cm^(-2),whereas that of a high Cu thickness achieves a high ethylene selectivity of 66.8%at267.2 m A cm^(-2)in the flow cell.Further structure-performance correlation and in-situ electrospectroscopic measurements attribute the high methane selectivity to isolated Cu clusters with low packing density and monotonous lattice structure,and the high ethylene efficiency to coalesced Cu nanoparticles with rich grain boundaries and lattice defects.The high Cu packing density and crystallographic diversity is of essence to promoting C–C coupling by stabilizing*CO and suppressing*H coverage on the catalyst surface.This work highlights the implementation of scientific and mathematic methods to uncover optimal catalysts and mechanistic understandings toward selective electrochemical CO_(2)reduction.

Design of experiments:how to develop and optimize drug delivery systems

Objective:Quality by design integration is exceedingly imperative for industries dealing with pharmaceuticals,but it diminishes product variability and delivers an extraordinary degree of assurance that the product would achieve the purpose for which it was formulated.The objective of the manuscript is to strengthen the understanding of the design of experimentation approach from the primary level.Hence,this review paper aims to get one experience with a course emphasizing product quality during its development process.Methods:The present work describes how experimental statistical designs can optimize the process.It is a strategy to improve the manufacturing of products and discuss the main factors involved in the production.The review describes different designs,advantages,disadvantages and design of experiments requirements concerning regulatory submissions.Results:Quality by design encourages the pharmaceutical industry to deal with risk management and proper understanding of products and manufacturing processes,assuring a good quality product.Having knowledge of quality by design and design of experiments in the formulation and process development will be beneficial for the optimization of drug delivery systems in upcoming times.Conclusion:Implementing quality by design at different phases in pharmaceutical manufacturing,the final product with a great degree of reproducible quality may be assured,depending upon experimental data.This contains valuable information in guiding new researchers about the importance and ways of using the design of experiments.

Multidisciplinary Design and Optimization of Satellite Launch Vehicle Using Latin Hypercube Design of Experiments

The design of new Satellite Launch Vehicle （SLV） is of interest, especially when a combination of Solid and Liquid Propulsion is included. Proposed is a conceptual design and optimization technique for multistage Low Earth Orbit （LEO） bound SLV comprising of solid and liquid stages with the use of Genetic Algorithm （GA） as global optimizer. Convergence of GA is improved by introducing initial population based on the Design of Experiments （DOE） Technique. Latin Hypercube Sampling （LHS）-DOE is used for its good space filling properties. LHS is a stratified random procedure that provides an efficient way of sampling variables from their multivariate distributions. In SLV design minimum Gross Lift offWeight （GLOW） concept is traditionally being sought. Since the development costs tend to vary as a function of GLOW, this minimum GLOW is considered as a minimum development cost concept. The design approach is meaningful to initial design sizing purpose for its computational efficiency gives a quick insight into the vehicle performance prior to detailed design.

Polylactic Acid (PLA) Foaming: Design of Experiments for Cell Size Control

In this study, a design of experiments (DoE) approach was used to develop a PLA open-cell foam morphology using the compression molding technique. The effect of three molding parameters (foaming time, mold opening temperature, and weight concentration of the ADA blowing agent) on the cellular structure was investigated. A regression equation relating the average cell size to the above three processing parameters was developed from the DoE and the analysis of variance (ANOVA) was used to find the best dimensional fitting parameters based on the experimental data. With the help of the DoE technique, we were able to develop various foam morphologies having different average cell size distribution levels, which is important in the development of open-cell PLA scaffolds for bone regeneration for which the control of cell morphology is crucial for osteoblasts proliferation. For example, at a constant ADA weight concentration of 5.95 wt%, we were able to develop a narrow average cell size distribution ranging between 275 and 300 μm by varying the mold opening temperature between 106°C and 112°C, while maintaining the foaming time constant at 8 min, or by varying the mold foaming time between 6 and 11 min and maintaining the mold opening temperature at 109°C.

Optimization of product characteristics of porous carbon agglomerates using a design of experiments in fluidized bed agglomeration

Spraying parameters during particle agglomeration processes can affect the agglomeration kinetics and particle growth.This study was conducted to better understand the influence of the spraying parameters in a fluidized bed wet agglomeration process,and the influence on the stability characteristics of carbon tablets.A formulation based on fine carbon and peroxide powder,as well as carboxymethyl cellulose as a binder,was used to produce agglomerates in a first production step.Thereafter in a second production step carbon tablets with a high porosity were molded for the customer goods industry.The optimization of the compressive strength of these carbon tablets was the goal of the trials.Carbon agglomerates were produced with a laboratory scale granulator called“ProCell”and were compressed with a five-cavity mechanical press.The screening of the agglomeration process parameters and their influence on the agglomerates quality,as well as the performance characteristics of the carbon tablets,were investigated using a multilevel factorial design.The experimental runs were done by varying atomized air pressure and feed rate of the fluid.This was determined by the design model.The findings of the statistical trials showed that low atomized air pressure and a low feed rate lead to a higher tablet compressive strength.

Factorial Experimental Design to Study the Effects of Layers and Fiber Content on Concrete Flexural Behavior

Experimentation has come a long in helping researchers achieve breakthroughs in their different scientific areas and engineering happens to be one of those areas with the most impact from experimental advancement. The need for valid experimental results free from biases and confounding conclusions has prompted the development of new experimental techniques that takes consideration of all applicable factor and combinations in providing answers on a research topic, and the Factorial Experimental design credited to Sir Ronald Fisher is one technique yielding highly valid results. This paper uses the factorial design of experiments to research the flexural impact of polyvinyl acetate fiber and layered concrete in construction. The experiment considered two levels of fiber contents and two levels of layers, and prepared samples with all combinations of the variable factors. The samples were tested after 7 days from casting for flexural strength and an advance statistical analysis was performed on the flexural responses of the samples using R-program. The results from the analyses revealed the significance of the variables to the flexural strength of the samples, as well as their interactions. The experiment concluded that based on the number of layers and fiber content used for the experiment, casting concrete in layers does have a significant negative effect on the flexural strength of concrete, and the failure pattern of concrete members under flexural load in evidently influenced by the material composition of the concrete, and that it can be evidently influenced by casting the concrete in layers.

Improving the reaction mix of a Pichia pastoris cell-free system using a design of experiments approach to minimise experimental effort

A renaissance in cell-free protein synthesis(CFPS)is underway,enabled by the acceleration and adoption of synthetic biology methods.CFPS has emerged as a powerful platform technology for synthetic gene network design,biosensing and on-demand biomanufacturing.Whilst primarily of bacterial origin,cell-free extracts derived from a variety of host organisms have been explored,aiming to capitalise on cellular diversity and the advantageous properties associated with those organisms.However,cell-free extracts produced from eukaryotes are often overlooked due to their relatively low yields,despite the potential for improved protein folding and posttranslational modifications.Here we describe further development of a Pichia pastoris cell-free platform,a widely used expression host in both academia and the biopharmaceutical industry.Using a minimised Design of Experiments(DOE)approach,we were able to increase the productivity of the system by improving the composition of the complex reaction mixture.This was achieved in a minimal number of experimental runs,within the constraints of the design and without the need for liquid-handling robots.In doing so,we were able to estimate the main effects impacting productivity in the system and increased the protein synthesis of firefly luciferase and the biopharmaceutical HSA by 4.8-fold and 3.5-fold,respectively.This study highlights the P.pastoris-based cell-free system as a highly productive eukaryotic platform and displays the value of minimised DOE designs.

Experimental modeling of high-voltage corona discharge using design of experiments

Many studies,both experimental and numerical,were devoted to the electric current of corona discharge and some mathematical models were proposed to express it.As it depends on several parameters,it is diffi cult to find a theoretical or an experimental formula,which considers all the factors.So we opted for the methodology of experimental designs,also called Tagushi’s methodology,which represents a powerful tool generally employed when the process has many factors to consider.The objective of this paper is to model current using this experimental methodology.The factors considered were geometrical factors(interelectrode interval,surface of the grounded plane electrode,curvature radius of the point electrode),climatic factors(temperature and relative humidity),and applied high voltage.Results of experiments made it possible to obtain mathematical models and to analyse the interactions between all factors.

Methodology for ranking controllable parameters to enhance operation of a steam generator with a combined Artificial Neural Network and Design of Experiments approach

The operation of complex systems can drift away from the initial design conditions,due to environmental condi-tions,equipment wear or specific restrictions.Steam generators are complex equipment and their proper opera-tion relies on the identification of their most relevant parameters.An approach to rank the operational parameters of a subcritical steam generator of an actual 360 MW power plant is presented.An Artificial Neural Network-ANN delivers a model to estimate the steam generator efficiency,electric power generation and flue gas outlet temperature as a function of seven input parameters.The ANN is trained with a two-year long database,with training errors of 0.2015 and 0.2741(mean absolute and square error)and validation errors of 0.32%and 2.350(mean percent and square error).That ANN model is explored by means of a combination of situations proposed by a Design of Experiment-DoE approach.All seven controlled parameters showed to be relevant to express both steam generator efficiency and electric power generation,while primary air flow rate and speed of the dynamic classifier can be neglected to calculate flue gas temperature as they are not statistically significant.DoE also shows the prominence of the primary air pressure in respect to the steam generator efficiency,electric power generation and the coal mass flow rate for the calculation of the flue gas outlet temperature.The ANN and DoE combined methodology shows to be promising to enhance complex system efficiency and helpful whenever a biased behavior must be brought back to stable operation.

DADOS:A Cloud-based Data-driven Design Optimization System

This paper presents a cloud-based data-driven design optimization system,named DADOS,to help engineers and researchers improve a design or product easily and efficiently.DADOS has nearly 30 key algorithms,including the design of experiments,surrogate models,model validation and selection,prediction,optimization,and sensitivity analysis.Moreover,it also includes an exclusive ensemble surrogate modeling technique,the extended hybrid adaptive function,which can make use of the advantages of each surrogate and eliminate the effort of selecting the appropriate individual surrogate.To improve ease of use,DADOS provides a user-friendly graphical user interface and employed flow-based programming so that users can conduct design optimization just by dragging,dropping,and connecting algorithm blocks into a workflow instead of writing massive code.In addition,DADOS allows users to visualize the results to gain more insights into the design problems,allows multi-person collaborating on a project at the same time,and supports multi-disciplinary optimization.This paper also details the architecture and the user interface of DADOS.Two examples were employed to demonstrate how to use DADOS to conduct data-driven design optimization.Since DADOS is a cloud-based system,anyone can access DADOS at www.dados.com.cn using their web browser without the need for installation or powerful hardware.

Application of experimental design techniques to structural simulation meta-model building using neural network

Neural networks are being used to construct meta-models in numerical simulation of structures.In addition to network structures and training algorithms,training samples also greatly affect the accuracy of neural network models.In this paper,some existing main sampling techniques are evaluated,including techniques based on experimental design theory, random selection,and rotating sampling.First,advantages and disadvantages of each technique are reviewed.Then,seven techniques are used to generate samples for training radial neural networks models for two benchmarks:an antenna model and an aircraft model.Results show that the uniform design,in which the number of samples and mean square error network models are considered,is the best sampling technique for neural network based meta-model building.

Decoding of Factorial Experimental Design Models Implemented in Production Process

The paper deals with factorial experimental design models decoding.For the ease of calculation of the experimental mathematical models,it is convenient first to code the independent variables.When selecting independent variables,it is necessary to take into account the range covered by each.A wide range of choices of different variables is presented in this paper.After calculating the regression model,its variables must be returned to their original values for the model to be easy recognized and represented.In the paper,the procedures of simple first order models,with interactions and with second order models,are presented,which could be a very complicated process.Models without and with the mutual influence of independent variables differ.The encoding and decoding procedure on a model with two independent first-order parameters is presented in details.Also,the procedure of model decoding is presented in the experimental surface roughness parameters models’determination,in the face milling machining process,using the first and second order model central compositional experimental design.The simple calculation procedure is recommended in the case study.Also,a large number of examples using mathematical models obtained on the basis of the presented methodology are presented throughout the paper.

A Multi-Objective Optimal Experimental Design Framework for Enhancing the Efficiency of Online Model Identification Platforms

Recent advances in automation and digitization enable the close integration of physical devices with their virtual counterparts, facilitating the real-time modeling and optimization of a multitude of processes in an automatic way. The rich and continuously updated data environment provided by such systems makes it possible for decisions to be made over time to drive the process toward optimal targets. In many man- ufacturing processes, in order to achieve an overall optimal process, the simultaneous assessment of mul- tiple objective functions related to process performance and cost is necessary. In this work, a multi- objective optimal experimental design framework is proposed to enhance the ef ciency of online model-identi cation platforms. The proposed framework permits exibility in the choice of trade-off experimental design solutions, which are calculated online that is, during the execution of experiments. The application of this framework to improve the online identi cation of kinetic models in ow reactors is illustrated using a case study in which a kinetic model is identi ed for the esteri cation of benzoic acid and ethanol in a microreactor.

Optimization by the Taguchi Method of a Robust Synthesis Protocol of Calcium Carbonate Phosphate

The experimental processes are difficult to model by physical laws, because a multitude of factors can intervene simultaneously and are responsible for their instabilities and their random variations. Two types of factors are to be considered;those that are easy to manipulate according to the objectives, and those that can vary randomly (uncontrollable factors). These could eventually divert the system from the desired target. It is, therefore, important to implement a system that is insensitive to fluctuations in factors that are difficult to control. The aim of this study is to optimize the synthesis of an apatitic calcium carbonate phosphate characterized with a Ca/P ratio equal to 1.61 by using the experimental design method based on the Taguchi method. In this process, five factors are considered and must be configured to achieve the previously defined objective. The temperature is a very important factor in the process, but difficult to control experimentally, so considered to be a problem factor (noise factor), forcing us to build a robust system that is insensitive to the last one. Therefore, a much simpler model to study the robustness of a synthetic solution with respect to temperature is developed. We have tried to parameterize all the factors considered in the process within a wide interval of temperature variation (60˚C - 90˚C). Temperature changes are no longer considered as a problem for apatitic calcium carbonate phosphate synthesis. In this finding, the proposed mathematical model is linear and efficient with very satisfactory statistical indicators. In addition, several simple solutions for the synthesis of carbonate phosphate are proposed with a Ca/P ratio equal to 1.61.

Kinetics of phenol and m-cresol biodegradation by an indigenous mixed microbial culture isolated from a sewage treatment plant

An acclimatized mixed microbial culture,predominantly Pseudomonas sp.,was enriched from a sewage treatment plant,and its potential to simultaneously degrade mixtures of phenol and m-cresol was investigated during its growth in batch shake flasks.A 22 full factorial design with the two substrates at two different levels and different initial concentration ranges(low and high),was employed to carry out the biodegradation experiments.The substrates phenol and m-cresol were completely utilized within 21 h when ...

Optimization of flotation variables for the recovery of hematite particles from BHQ ore

The technology for beneficiation of banded iron ores containing low iron value is a challenging task due to increasing demand of quality iron ore in India. A flotation process has been developed to treat one such ore, namely banded hematite quartzite （BHQ） containing 41.8wt% Fe and 41.5wt% SiO2,by using oleic acid, methyl isobutyl carbinol （MIBC）, and sodium silicate as the collector, frother, and dispersant, respectively. The relative effects of these variables have been evaluated in half-normal plots and Pareto charts using central composite rotatable design. A quadratic response model has been developed for both Fe grade and recovery and optimized within the experimental range. The optimum reagent dosages are found to be as follows： collector concentration of 243.58 g/t, dispersant concentration of 195.67 g/t, pH 8.69, and conditioning time of 4.8 min to achieve the maximum Fe grade of 64.25% with 67.33% recovery. The predictions of the model with regard to iron grade and recovery are in good agreement with the experimental results.

Optimization and Sizing for Propulsion System of Liquid Rocket Using Genetic Algorithm

Flight vehicle conceptual design appears to be a promising area for application of the Genetic Algorithm （GA） as an approach to help to automate part of the design process. This computational research effort strives to develop a propulsion system design strategy for liquid rocket to optimize take-off mass, satisfying the mission range under the constraint of axial overload. The method by which this process is accomplished by using GA as optimizer is outlined in this paper. Convergence of GA is improved by introducing initial population based on Design of Experiments Technique.

Statistical optimization of stress level in Mg-Li-Al alloys upon hot compression testing

In the present study,a response optimization method using Extreme Vertices Mixer Design(EVMD)approach is proposed for stress optimization in a thermomechanically processed Mg-Li-Al alloy.Experimentation was planned as per mixed design proportions of Mg,Li and Al and process variables(i.e.temperature and strain rate).Each experiment has been performed under different conditions of factors proportions and process variables.The response,particularly stress has been considered for each experiment.The response is optimized to find an optimum condition when the contributing factors influence material characteristics in such a way,to achieve better strength,ductility and corrosion resistance.Estimated regression coefficient table for response has been observed to identify the important factors in this process and significantly high variance inflation factor has been observed.Most importantly,an optimum condition is achieved from this analysis which fulfills the experimental observations and theoretical assumptions.

Effect of Phenolation, Lignin-Type and Degree of Substitution on the Properties of Lignin-Modified Phenol-Formaldehyde Impregnation Resins: Molecular Weight Distribution, Wetting Behavior, Rheological Properties and Thermal Curing Profiles

Here,the effects of substituting portions of fossil-based phenol in phenol formaldehyde resin by renewable lignin from two different sources are investigated using a factorial screening experimental design.Among the resins consumed by the wood-based industry,phenolics are one of the most important types used for impregnation,coating or gluing purposes.They are prepared by condensing phenol with formaldehyde(PF).One major use of PF is as matrix polymer for decorative laminates in exterior cladding and wet-room applications.Important requirements for such PFs are favorable flow properties(low viscosity),rapid curing behavior(high reactivity)and sufficient self-adhesion capacity(high residual curing potential).Partially substituting phenol in PF with bio-based phenolic co-reagents like lignin modifies the physicochemical properties of the resulting resin.In this study,phenol-formaldehyde formulations were synthesized where either 30%or 50%(in weight)of the phenol monomer were substituted by either sodium lignosulfonate or Kraft lignin.The effect of modifying the lignin material by phenolation before incorporation into the resin synthesis was also investigated.The resins so obtained were characterized by Fourier Transform Infra-Red(FTIR)spectroscopy,Size Exclusion Chromatography(SEC),Differential Scanning Calorimetry(DSC),rheology,and measurements of contact angle and surface tension using the Wilhelmy plate method and drop shape analysis.