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The purpose of this paper is to investigate the electrochemical treatment of textile dye wastewater in the presence of NaCl electrolyte by using aluminium electrodes.

The electrochemical treatment of textile dye wastewater was optimized using response surface methodology (RSM). RSM‐based D‐optimal design was employed to construct statistical models relating turbidity and designed effective parameters known as current density, electrolyte concentration and electrolysis time. The experimental plan consists of a three‐factor (three numerical) matrix.

The results show that the current density has significant effect on the reduction of turbidity. Besides, electrolysis time is the most influential factor on the turbidity. In order to enhance the electrochemical treatment performance, no coagulant addition or further physicochemical processes were employed.

Industrial certain textile dye wastewater in Turkey is used to determine optimal values.

Currently, the state of practice in experimental design centres on orthogonal designs (Alpizar et al., 2003), which are suitable when applied to surveys with a large sample size. In a stated choice experiment involving interdependent freight stakeholders in Sydney (see Hensher & Puckett, 2007; Puckett et al., 2007; Puckett & Hensher, 2008), one significant empirical constraint was difficult in recruiting unique decision-making groups to participate. The expected relatively small sample size led us to seek an alternative experimental design. That is, we decided to construct an optimal design that utilised extant information regarding the preferences and experiences of respondents, to achieve statistically significant parameter estimates under a relatively low sample size (see Bliemer & Rose, 2006).

The D-efficient experimental design developed for the study is unique, in that it centred on the choices of interdependent respondents. Hence, the generation of the design had to account for the preferences of two distinct classes of decision makers: buyers and sellers of road freight transport. This paper discusses the process by which these (non-coincident) preferences were used to seed the generation of the experimental design, and then examines the relative power of the design through an extensive bootstrap analysis of increasingly restricted sample sizes for both decision-making classes in the sample. We demonstrate the strong potential for efficient designs to achieve empirical goals under sampling constraints, whilst identifying limitations to their power as sample size decreases.

A general approach for the construction of global approximations to structural behaviour using response surface methodology is presented. The computation and use of these approximations are demonstrated using a semi‐solid tyre example. The use of these global approximations to the responses made it viable to utilise the capabilities of non‐linear analysis software in design optimisation. The insight gained from a preliminary low fidelity model was utilized in a two‐stage approach to achieve the maximum benefit from a more expensive high fidelity model. The resulting high‐accuracy approximations greatly reduced the cost of subsequent design calculations such as multidisciplinary and discrete optimisation.

In a structural optimization design-based single-level response surface, the number of optimal variables is too much, which not only increases the number of experiment times, but also reduces the fitting accuracy of the response surface. In addition, the uncertainty of the optimal variables and their boundary conditions makes the optimal solution difficult to obtain. The purpose of this paper is to propose a method of fuzzy optimization design-based multi-level response surface to deal with the problem.

The main optimal variables are determined by Monte Carlo simulation, and are classified into four levels according to their sensitivity. The linear membership function and the optimal level cut set method are applied to deal with the uncertainties of optimal variables and their boundary conditions, as well as the non-fuzzy processing is carried out. Based on this, the response surface function of the first-level design variables is established based on the design of experiments. A combinatorial optimization algorithm is developed to compute the optimal solution of the response surface function and bring the optimal solution into the calculation of the next level response surface, and so on. The objective value of the fourth-level response surface is an optimal solution under the optimal design variables combination.

The results show that the proposed method is superior to the traditional method in computational efficiency and accuracy, and improves 50.7 and 5.3 percent, respectively.

Most of the previous work on optimization was based on single-level response surface and single optimization algorithm, without considering the uncertainty of design variables. There are very few studies which discuss the optimization efficiency and accuracy of multiple design variables. This research illustrates the importance of uncertainty factors and hierarchical surrogate models for multi-variable optimization design.

There are process uncertainties and material property variations during laminated steel sheet forming, and those fluctuations may result in non-reliable forming quality issues such as fracture and delamination. Additionally, the optimization of sheet forming process is a typical multi-objective optimization problem. The target is to find a multi-objective design optimization and improve the process design reliability for laminated sheet metal forming. The paper aims to discuss these issues.

Desirability function approach is adopted to conduct deterministic multi-objective optimization, and response surface is used as meta-model. Reliability analysis is conducted to evaluate the robustness of the multi-objective design optimization. The proposed method is implemented in a step-bottom square cup drawing process. First, forming process parameters and three noise factors are assumed as probability variables to conduct reliability assessment of the laminated steel sheet forming process using Monte Carlo simulation. Next, only two forming process parameters, blank holding force and frictional coefficient, are considered as probability variables to investigate the influence of the forming parameter deviation on the variance of the response using the first-order second-moment method.

The results indicate that multi-objective design optimization using desirability function method has high efficiency, and an optimized robust design can be obtained after reliability assessment.

The proposed design procedure has potential as a simple and practical approach in the laminated steel sheet forming process.

The purpose of this article is to provide an extensive review of the barriers faced by engineers when applying design of experiments (DoE). The aim is to help new practitioners learn from the past and avoid possible barriers that they may encounter when applying DoE in industry.

An exhaustive literary review was carried out to find articles in which hindrances to the application of DoE were mentioned. The information is organised and grouped into 16 barriers with this end in mind.

The 16 barriers can be classified into three different groups: business barriers; educational barriers; and technical barriers. It is shown that DoE can be successfully applied without overcoming every barrier, although it is inconvenient to do so.

Although DoE is commonly found in statistics and quality literature, it is clearly underused in industry. The paper brings together ideas from those with experience in DoE to detect the reasons behind this anomaly.

Very little material has been published regarding the difficulty of applying DoE. Unfortunately, what is available is repetitive, unstructured and incomplete. The paper is intended to encourage discussion between practitioners and experts, in order to find a way to define, categorise and eventually overcome the most problematic barriers.

The purpose of this paper is to develop a high‐throughput approach to optimize printing of pigment‐based formulations.

A total of 40 formulations were robotically prepared by varying the concentrations of diethyleneglycol, glycerol and surfynol. In addition, a variety of inkjet printer (process) variables (voltage, pulse width and frequency) was varied. The combined influence of these two sets of variables on printing performance were determined, analysed and optimised using the Statistical Software Package (MODDE 8), which uses multiple linear regression and partial least square analysis.

The components diethyleneglycol and surfynol were observed to predominantly control viscosity and surface tension of all formulations, which voltage and pulse width were found to be the main factors controlling the spread of the droplet on the substrate.

Optimisation of pigment‐based formulations has typically involved the one‐by‐one systematic variation of components in a stepwise manner. The work reported here allowed the generation of a robust model allowing the properties of any new formulation to be accurately predicted. Importantly, the experimental tools and methods developed can be applied quite generally to the preparation of any new formulation for inkjet printing application.

Experimental design and high‐throughput technology allow new formulations to be accurately predicted for diverse inkjet applications.

The purpose of this paper is to model the corrosion rate behavior for two ferrous materials, carbon steel AISI 1020 and stainless steel AISI 304, immersed in ferric sulfate and ferric chloride solutions using D-optimal design with response surface methodology.

Experimental design addresses two factors (concentration and contact time) with multilevel categories, in order to predict and compare the corrosion rates of the studied materials immersed in flocculants solutions. A corrosion rate of specimens was calculated from mass loss determinations.

The authors used a polynomial model to fit the experimental values, thereby predicting significantly higher corrosion rates in ferric chloride solutions, as compared to ferric sulfate.

The authors propose a high fidelity model of the corrosion rate of each carbon steel and stainless steel material using D-optimal design with a response surface method (RSM).

Robust design is a well‐known quality improvement method that focuses on building quality into the design of products and services. Yet, most well established robust design models only consider a single performance measure and their prioritization schemes do not always address the inherent goal of robust design. This paper aims to propose a new robust design method for multiple quality characteristics where the goal is to first reduce the variability of the system under investigation and then attempt to locate the mean at the desired target value.

The paper investigates the use of a response surface approach and a sequential optimization strategy to create a flexible and structured method for modeling multiresponse problems in the context of robust design. Nonlinear programming is used as an optimization tool.

The proposed methodology is demonstrated through a numerical example. The results obtained from this example are compared to that of the traditional robust design method. For comparison purposes, the traditional robust design optimization models are reformulated within the nonlinear programming framework developed here. The proposed methodology provides enhanced optimal robust design solutions consistently.

This paper is perhaps the first study on the prioritized response robust design with the consideration of multiple quality characteristics. The findings and key observations of this paper will be of significant value to the quality and reliability engineering/management community.