Design And Analysis Of Ecological Experiments Pdf

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Design And Analysis Of Ecological Experiments Pdf

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Native Plants For Greening Mediterranean Agroecosystems

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Received: 2018 February 16 / Revised: 2019 January 14 / Accepted: 2019 January 15 / Published: 2019 January 17

Designing and conducting effective and informative experiments in comparative water treatment studies is challenging due to their complexity and diversity. Often, environmental engineers and researchers carefully plan their experiments based on literature information, available equipment and time, analytical methods, and hands-on activities. However, due to time constraints and a severe lack of understanding, they neglect preliminary experiments as well as statistical and modeling techniques in experimental design. In this paper, the important implications of these overlooked processes are presented in a practical framework, focusing on comparative studies of water treatment optimization. By combining detailed experimental planning, laboratory testing, and advanced data analysis, more relevant decisions and recommendations can be made, allowing us to build on the results of quantitative and extensive experiments. The process demonstrates the importance of three key steps, including pre-training, predictive modeling and statistical analysis, which are strongly recommended to avoid sub-optimal designs and even failed experiments that lead to wasted resources and disappointing results. The application and relevance of this methodology is demonstrated in a case study comparing the performance of activated sludge and waste retention ponds under a shock loading scenario. Therefore, we advise that when conducting an experimental project, the aim is to make the best use of statistics and modeling tools, but not to lose sight of the scientific understanding and practical possibilities of water treatment processes.

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Water treatment is an important water reuse and management technology. In the last century, drinking water treatment and wastewater treatment technologies have been rapidly developed to cope with increasing water consumption, mainly due to the industrial revolution and urban development [1]. In order to improve removal and energy efficiency, environmental engineers have focused on the best technologies, comparing or contrasting different treatments. In particular, water treatment can be improved by changing five elements: (1) treatment technologies, (2) system configurations, (3) testing procedures, (4) system inputs, and (5) conditions. work (Fig. 1). Treatment progress can be demonstrated using three index systems that reflect the balanced sustainability of water technologies, including environmental performance, economic efficiency, and social stability [2].

Regarding multidisciplinary aspects such as physics, chemistry, microbiology and bioprocess technology, water treatment research is based on experimental observations [3]. Three important elements must be carefully considered in these experiments: (1) analytical methods, (2) experimental design, and (3) statistical analysis. The first two elements are well described in many popular literature or guidelines, such as APHA’s Recommended Methods for Water and Water Analysis [4] and van Loosdrecht, Nielsen, Lopez-Vazquez and Brdjanovic’s Test Methods in Water Treatment [3]. On the other hand, to the authors’ knowledge, there is no standardized procedure for statistical analysis in comparative water treatment studies. Interestingly, in many other fields, such as geography, evolution, biology or the behavioral sciences, researchers agree that experimental design and statistical analysis are closely related. Therefore, before conducting experiments and collecting data, it is important to carefully think through the exact wording of research questions and clearly translate them into statistical hypotheses [5].

This article aims to provide a practical basis for experimental design in comparative water treatment research. Throughout this process, we highlight the key steps in test planning where the connection to statistics is most important. In addition, the process also emphasizes the role of modeling and simulation as a companion to real experiments in the virtual world, as these tools can be very effective, but are rarely used in an experimental design. To keep the size of this book under control and not get lost in the confusion of statistics and modeling, we keep the process as simple as possible, so we describe in detail some of the basic principles of experimental design, such as blocking, randomization, design. , and factor. the design found in many textbooks is absent. Finally, to ensure that the testers find the process simple, easy to use and truly useful, its application is demonstrated in a case study comparing the performance of activated sludge (CAS) and waste retention pond (WSP). peak load event. .

The first phase of the process consists of four main steps with 10 modules and two feedback loops (Figure 2). Each of these levels will be discussed in subsequent chapters, with a particular focus on sector-specific modules.

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At the beginning, not only the research problem must be clearly and concisely stated, but also the research objectives must be formulated in detail. Experimenters should think of themselves as managers who need to be informed about the objectives of the study in order to develop their own action plans during the experiment. To do this, Doran [6] suggested that specific activities should be carried out according to S.M.A.R.T. approach: specific, measurable, attributable, realistic and time-bound. As shown in Figure 1, the types of comparisons and benchmarks can be decided, and the list of response variables for experiments can be compiled from benchmarks. The most common variables of interest in water technology are treatment removal efficiency with indicators of organic matter, nutrients, heavy metals, pathogens or emerging pollutants. The number of response variables is determined by the objectives of the study and the available resources, including money, time, and manpower, that are needed or already available for sample collection and analysis.

Due to financial and time pressures on research, the preliminary study or pilot experiment is often neglected despite its important role in the experimental design. First, it allows auditors to follow treatment plans and analytical procedures. Due to pretreatment, systems can perform more stably and accurately in the real test phase, especially when experiments involve complex analytical procedures, advanced processing equipment or bacterial infection. Second, information from preliminary studies can be used to make reasonable predictions about what might happen in full-scale experiments. This prediction is presented as a prediction model in step 4 (see 2.2.1 below). By reducing the chance of experiment failure, we can save more money and time. In addition, these pilot experiments also provide information about the experimental error and data variability needed for the sample size calculation in step 6 (see 2.2.3 below).

Design factors are parameters that may influence treatment performance in general and response variables in particular. These parameters can be divided into five subcategories, as shown in Figure 1, including technical types, configuration settings, test methods, system inputs, and operating conditions. By considering them and their interactions as potential design factors, industrial experiments can be developed. Such a design can be used for various purposes. A screening test usually examines several factors simultaneously to select the most important factors to be investigated in detail. For selection, continuous parameters are discretized into a factor variable with two or three levels (eg, “low” and “high” or “low”, “medium” and “high”) and usually only linear effects. we checked. When the number of factors k is not too large, full designs can be performed, but note that the total number of 2k experiments for a full factory design increases exponentially with k (for example, when k = 10.2).

= 59, 049 experiments are required). Therefore, you may want to consider a partition design.

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