General and Unifying Concepts for Wastewater Treatment Plant Design

世界上大约有一半的人居住在城市，而且这一比例还在上升。世界各地的社会都希望工程师通过清洁或管理我们的土壤、水和空气的质量来提供可持续的城市生活;清理我们今天产生的污染物和前几代人造成的污染。世纪以来，工程师们已经转向生物处理系统来帮助他们完成这项任务。这些系统可用于清洁家庭和工业废水、受污染的地下水、气味源、城市垃圾和受污染的土地。工程师的天才和工程学的悖论是，即使我们不完全理解它们是如何工作的，系统也可以被成功地操纵。对于工程生物系统来说尤其如此，工程师必须在仅仅粗略了解自然界最基本的概念和处理废物的微生物活动的情况下进行操作。我们所知道的很多东西都是通过经验（试错）研究学到的，我们所利用的理论基础充其量也就是基于20世纪40年代的科学。工程史告诉我们，改进设计背后的理论有助于工程实践的革命（比较18世纪末的桥梁和19世纪末的桥梁）。我们相信，如果环境工程师能够改进设计的理论基础，我们将对生物处理系统的设计产生类似的根本影响。研究生物体与其环境之间关系的学科称为生态学。因此，我们相信理论生态学是我们可以从中学到最多的科学。因此，我们试图利用生态学理论来提高我们预测系统的三个方面的能力：物种的性质，混沌动力学的存在（或其他），污染物消耗和工程师对资源的操纵之间的关系。我们认为，生物处理系统的成功和失败往往与生物处理的这三个相互关联的方面之一有关。使用最初开发的想法来预测热带森林和热带岛屿的组成，我们已经成功地开发了一个模型，使我们能够预测生物处理系统中细菌的数量和可能的身份。这种模式似乎适用于各种各样的情况，很可能具有普遍性。我们想正式证实这个模型，并为广泛的工程系统进行校准，看看它是否能告诉我们废水中内分泌干扰化合物的命运。这个模型的结果可以直接输入到我们与欧洲各地的同事正在开发的另一个模型中，以预测这些物种的微尺度生长如何转化为硝化生物膜和微生物燃料电池中的宏观行为。我们已经开发并初步采用生物处理系统中的混沌动力学测试。我们需要完成这些测试，并用可能导致混乱的数学模型来支持它们，看看我们是否能将基本生物学与系统的稳定性联系起来。我们还开发了模型，将资源可用性与系统中的故障概率联系起来。目前，这可用于确定给定通气率的概率，但它可以更广泛地使用。主要问题是模型的校准。校准意味着确定单个物种的丰度。这在目前是可能的，但进展缓慢。需要更快更简单的方法，寻找这种方法正在成为我们的优先事项。我们的建议是困难的，需要大量的数据和专业技能。我们通过国际合作加快了我们的进展并增加了我们的资金，我们还通过讲习班传播了我们的技能。我们将继续合作和训练，为我们的目标而努力。

About half the world lives in cities and the proportion is rising. Societies across the world look to engineers to provide a sustainable urban existence by cleaning or managing the quality of our soils water and air; cleaning up the pollutants we produce today and the pollution created by earlier generations. For over a century now engineers have turned to biological treatment systems to help them in this task. These systems can be used to clean domestic and industrial wastewater, contaminated groundwater, sources of smell, municipal refuse and contaminated land. The genius of engineers and the paradox of engineering is that systems can be successfully manipulated even though we don't fully understand how they work. This is particularly true for engineered biological systems where engineers must operate with only a sketchy of how even the most basic concepts of the nature abundance and activities microbes that treat the waste. Much of what we know has been learnt by empirical (trial and error) research and theoretical foundation we exploit is at best often based on science from the 1940s. The history of engineering tells us that improving the theory behind design can help revolutionise engineering practise (compare the bridges of the late 18th century with the bridges of the late 19th century). We believe that, if environmental engineers can improve the theoretical basis of design, we have similarly radical effect on the design of biological treatment systems. The study of the relationship between an organism and its environment is called ecology. We therefore believe that theoretical ecology is the science from which we can learn the most. We have therefore sought to use ecological theory to improve our ability to predict three aspects of a system: the nature of the species present, the presence (or otherwise) of chaotic dynamics, the relationship between pollutant consumption and resource manipulation by the engineer. We believe that success and failure in a biological treatment system often relates to one of these three interlinked aspects of biological treatment. Using ideas originally developed to predict the composition of tropical forests and tropical islands, we has successfully developed a model that allow us to predict the number and possible identity of bacteria in biological treatment systems. This model appears to work in a very wide variety of situations and may well be universal. We would like to formally corroborate this model, and calibrate it for a wide range of engineered systems and see if it will tell us something about the fate of endocrine disrupting compounds in wastewater. The out come of this model can be fed directly into another model that we are developing with colleagues all over Europe to predict how microscale growth of these species translates into the macroscale behaviour in nitrifying biofilms and microbial fuel cells. We have developed and tentatively employed tests of chaotic dynamics in biological treatment systems. We need to complete those tests and back them up with mathematical models of possible causes of chaos to see if we can relate the basic biology to the stability of the system. We have also developed models to relate resource availability to the probability of failure in a system. At present this can be used to determine the probability of for a given aeration rate however it could be used much more widely. The major problem is the calibration of the models. Calibration means determining the abundance of individual species. This is possible at present but slow. Faster simpler methods are needed and the search for such methods is emerging as a priority for us. What we propose is difficult and requires large amounts of data and specialist skill. We have accelerated our progress and enhance our funding by cooperating internationally and we have disseminated our skill through workshops. We will continue to both cooperate and train as we work towards our goal.