In this work, an orthogonal array experimental design was used to optimize the synthesis of a photocatalyst. This chapter provides the reader a crucial foundation for understanding the terminology and practical use of design of experiments (DOE). The practical use of this, as will be discussed later in this work, is that wise use of DOE can drastically reduce the time and effort to optimize procedures, catalyst synthesis or otherwise. In this section, we explore some of the general procedures of experimental design, as well as several commonly-used designs. We then examine different data analysis techniques – the column effects method, and ANOVA. We also present some history on orthogonal array designs, and how they are useful at cutting cost and time investment in research.
1.1 Basic Definition of Design of Experiments
The phrase “design of experiments” refers to any orderly plan, or design, that describes four key features of an experiment, as summarized by Finney [1]:
i. The set of factors to be formed into treatments.
ii. What the test subjects will be.
iii. The rules for applying treatments to the test subjects.
iv. What measurements will be taken before, during, and/or after the treatments have been applied to the test subjects.
At each step above, the experimenter should bear in mind the impact that his or her decisions will have on the cost, feasibility, and precision of the experiment [1].
1.2 An Example Chemical Engineering DOE Problem
Though good experimental design is important for producing reliable, reproducible research, many engineers are unfamiliar with DOE concepts, and many of the terms in the previous section may seem unfamiliar. For didactic purposes, we present a simple example that...
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... would need 2 × 34 = 162 observations to be able to fully explore the parameter space. Due to this “combinatorial explosion”, it is more common in scientific and industrial practice is to use a fractional-factorial experiment (FFE).
1.5 Fractional Factorial Designs and Orthogonal Arrays
Despite conveying less information than an FFD, it is possible for an FFE to capture a large amount of the variation in the data with fewer experimental trials. The justification for using FFE's comes from the sparsity of effects principle, which states that the effect of higher-order interactions, though ubiquitous, are usually insignificant [4]. Though we confound the main effects with these other interactions, this confounding is probably negligible, and thus, we can justify reducing the number of required runs. An especially popular type of FFE is the orthogonal-array design.
The results of this experiment are shown in the compiled student data in Table 1 below.
1995). Kolotkin et al. (1995) built their experiment on the belief that, “monitoring factors suc...
Possible sources of error in this experiment include the inaccuracy of measurements, as correct measurements are vital for the experiment.
To develop problem solving and experimental skills, for example, information is accurately processed and presented, experimental procedures are planned, designed and evaluated properly, producing valid results, recording results, and valid conclusion is drawn.
In order to have a successful, reliable experiment you need sufficient data and evidence, reliable research, variables to test and a follow – up experiment. There are several types of variables you need to do an experiment. An independent variable is the manipulated experimental factor that is changed to see what the effects are. A dependent variable is the outcome. This factor can change in an experiment in reaction to the changes in the independent variable. An experimental group is the group of participants that are exposed to the change that the independent variable represents. The control group is participants who are treated in the same way as the experimental group except for the manipulated factor which is the independent variable (King 24). Proper data, evidence and research is also needed so the experiment turns out correctly and you know what you are testing. A follow – up experiment is not required, however it helps the validity of the conclusion of the experiment. Validity is “the soundness of the conclusions that a researcher draws from an experiment” (King 25). Conducting a follow – up experiment will help researchers and people alike see if the experiment worked properly, continues to help people and see how participants are doing after the experiment is over.
Going into details of the article, I realized that the necessary information needed to evaluate the experimental procedures were not included. However, when conducting an experiment, the independent and dependent variable are to be studied before giving a final conclusion.
To develop problem solving and experimental skills, for example, information is accurately processed, using calculations where appropriate, experimental procedures are planned, designed and evaluated properly, the use of microscopes, producing valid results and recording results.
4. Performance of experimental tests of the predictions by several independent experimenters and properly performed experiments.
The experimental design of the research involves the organization of an experiment to effectively test the study’s hypothesis. In addition, it involves setting up proper manipulations and measurements of an experiment. To test this specific hypothesis, the researcher will need significant resources, such as direct scanners, to test and analyze the variables. The variables in the study will include the plasticity of the human brain during different life stages and the age differences between individuals. The experimental design includes independent and dependent variables, which the researcher will thoroughly test and
However, a hypothesis cannot function without its independent and dependent variables. They are both parts of an experiment that are in place to be measured and experimented with. Many variables exist, fo...
Experimental designs are viewed as the most accurate, and most demanding of research designs, requiring strict attention to rules and procedures. Researchers use these research designs to manipulate and control testing procedures as a way to understand a cause and effect relationship. Commonly, independent variables are manipulated to judge or decide their effect on a dependent variable (Trochim & Donnelly, 2008).
...s strength in the experiment rather than a limitation which future studies should also monitor.
The laboratory experiment gives the experimenter a greater chance to control the conditions and enables you to measure behaviour with greater precision. This method also allows for quantative research and also enables greater control of variables. Although it gives the experimenter greater control, this can also seem daunting to the subject who may feel more uncomfortable and is less likely to ...
allowed to come up with and preform his or her experiment. In this experiment one will
Compero, L., D. Walker, E. E. Atienzo & J. P. Gutierrez. 2011. A quasi-experimental evaluation of