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.

Analyzing Laser-Welded NiTi-NiTi-Joints for Actuator Applications Using Design of Experiments

One promising joining method for NiTi-SMA （shape memory alloy）-components is laser welding. This joining technology bears huge potential regarding process automation and mechanical properties as well as durability, especially within the field of small- and medium-sized actuators. However, there is still need for research due to unsolved issues influencing the microstructure and thus effecting mechanical properties as well as SMA-characteristics of these joints. Therefore, the purpose of this paper is the evaluation of quality parameters of NiTi-NiTi-wire-joints. For this purpose, design of experiments with a fractional factorial design is used for the investigation, because of its high potential to decrease experimental effort. This paper provides a basis for future research in the field of SMA-actuators and joining.

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.

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.

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.

Optimizing Bucket Elevator Performance through a Blend of Discrete Element Method, Response Surface Methodology, and Firefly Algorithm Approaches

This research introduces a novel approach to enhancing bucket elevator design and operation through the integration of discrete element method(DEM)simulation,design of experiments(DOE),and metaheuristic optimization algorithms.Specifically,the study employs the firefly algorithm(FA),a metaheuristic optimization technique,to optimize bucket elevator parameters for maximizing transport mass and mass flow rate discharge of granular materials under specified working conditions.The experimental methodology involves several key steps:screening experiments to identify significant factors affecting bucket elevator operation,central composite design(CCD)experiments to further explore these factors,and response surface methodology(RSM)to create predictive models for transport mass and mass flow rate discharge.The FA algorithm is then applied to optimize these models,and the results are validated through simulation and empirical experiments.The study validates the optimized parameters through simulation and empirical experiments,comparing results with DEM simulation.The outcomes demonstrate the effectiveness of the FA algorithm in identifying optimal bucket parameters,showcasing less than 10%and 15%deviation for transport mass and mass flow rate discharge,respectively,between predicted and actual values.Overall,this research provides insights into the critical factors influencing bucket elevator operation and offers a systematic methodology for optimizing bucket parameters,contributing to more efficient material handling in various industrial applications.

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.

Optimization of mechanical properties using D-optimal factorial design of experiment: Electromagnetic stir casting process of A357-SiC nanocomposite

Electromagnetic stir casting process of A357-Si C nanocomposite was discussed using the D-optimal design of experiment(DODOE) method. As the main objective, nine random experiments obtained by DX-7 software were performed. By this method, A357-Si C nanocomposites with 0.5, 1.0 and 1.5 wt.% Si C were fabricated at three different frequencies(10, 35 and 60 Hz) in the experimental stage. The microstructural evolution was characterized by scanning electron and optical microscopes, and the mechanical properties were investigated using hardness and roomtemperature uniaxial tensile tests. The results showed that the homogeneous distribution of Si C nanoparticles leads to the microstructure evolution from dendritic to non-dendritic form and a reduction of size by 73.9%. Additionally, based on DODOE, F-values of 44.80 and 179.64 were achieved for yield stress(YS) and ultimate tensile strength(UTS), respectively, implying that the model is significant and the variables(Si C fraction and stirring frequency) were appropriately selected. The optimum values of the Si C fraction and stirring frequency were found to be 1.5 wt.% and 60 Hz, respectively. In this case, YS and UTS for A357-Si C nanocomposites were obtained to be 120 and 188 MPa(57.7% and 57.9 % increase compared with those of the as-cast sample), respectively.

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.

Practical Aspects for Designing Statistically Optimal Experiments

Due to operational or physical considerations, standard factorial and response surface method （RSM） design of experiments （DOE） often prove to be unsuitable. In such cases a computer-generated statistically-optimal design fills the breech. This article explores vital mathematical properties for evaluating alternative designs with a focus on what is really important for industrial experimenters. To assess ＂goodness of design＂ such evaluations must consider the model choice, specific optimality criteria （in particular D and I）, precision of estimation based on the fraction of design space （FDS）, the number of runs to achieve required precision, lack-of-fit testing, and so forth. With a focus on RSM, all these issues are considered at a practical level, keeping engineers and scientists in mind. This brings to the forefront such considerations as subject-matter knowledge from first principles and experience, factor choice and the feasibility of the experiment design.

Evaluation of Full Experimental Design Method against Fractional Design Method and Taguchi Design Method in Machining Operation

DOE （design of experiments） is a systematic, rigorous approach to engineering problem-solving that applies principles and techniques at the data collection stage so as to ensure the generation of valid, defensible, and supportable engineering conclusions. This paper presents a comparison of three different experimental designs （full experimental design, fractional design and Taguchi design） aimed at studying the effects of cutting parameters variations on surface finish. The results revealed that the effects obtained by analyzing both fractional and Taguchi designs were comparable to the main effects and two-level interactions obtained by the full factorial design. Thus, we conclude that full factorial design appear to be reliable and more economical since they permit to reduce by a factor the amount of time and effort required to conduct the experimental design without losing valuable information. Thus, we conclude that full factorial design appear to be reliable and more economical and without losing valuable information.

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.