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.
There were no significant error factors that may have affected the arrangement of the lab experiment. Everything went smoothly with relative ease.
The results of this experiment are shown in the compiled student data in Table 1 below.
Possible sources of error in this experiment include the inaccuracy of measurements, as correct measurements are vital for the experiment.
= I have decided to produce a step-by-step guide for each experiment. just to ensure that when we actually come to conducting the practical work, it runs flawlessly. This will also help us conduct fairer tests. as we will be following the same set of steps each time we collect a result.
Have you ever thought about the preparation and thought that goes into a research experiment? There are many things to consider when planning a study, such as the questions you are trying to answer, the variety of participants that will be studied, and the different variations in the experiment. An important part of the experiment that can have a significant impact on the results are the variables chosen. In doing this, the researcher can easily tell what factors have an effect on the topic under study.
Quasi-experimental designs are experimental designs that do not provide for the full control of extraneous variables. Primarily, the absence of control in this design is due to the lack of random assignment to groups. Quasi-experimental research designs are used in the study of cause and effect by manipulating the independent variable.
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.
...r a period of time, like blood pressure and quality of life, in order to better understand the course of the disease. No drugs are given, but other types of interventions are made during the time of the study. Interventional studies also monitor and record various factors over a period of time while testing experimental treatments, devices or combinations of drugs to see if the disease outcome is altered.
Planning Firstly here is a list of equipment I used. Boiling tubes Weighing scales Knife Paper towels 100% solution 0% solution (distilled water) measuring beakers potato chips Cork borer. We planned to start our experiment by doing some preliminary work. We planned to set up our experiment in the following way.
...s strength in the experiment rather than a limitation which future studies should also monitor.
... more experienced or give some training in a bid to avoid careless mistakes during the experiment.
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.
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 ...
In most studies, tracer tests are considered to be more accurate because they would better implement the hete...
There is also the potential of human error within this experiment for example finding the meniscus is important to get an accurate amount using the graduated pipettes and burettes. There is a possibility that at one point in the experiment a chemical was measured inaccurately affecting the results. To resolve this, the experiment should have been repeated three times.