M. Bruce Beck

A New Planning and Design Paradigm to Achieve Sustainable Resource Recovery from Wastewater

To employ technologies that sustainably harvest resources from wastewater (for example struvite granules shown here), new perceptions and infrastructure planning and design processes are required.

Modelling, simulation and control of urban wastewater systems

1. Introduction.- 1.1 Motivation of this Book.- 1.1.1 Administrative Responsibilities.- 1.1.2 Standards.- 1.1.3 Computer Software.- 1.1.4 Design and Operation.- 1.2 Outline of Chapters.- 2. The State of the Art.- 2.1 Components of the Urban Wastewater System: Basic Processes and Modelling Concepts.- 2.1.1 Urban Catchment Runoff and Sewer System.- 2.1.1.1 Surface Runoff in Urban Areas.- 2.1.1.2 Flow in the Sewer System.- 2.1.1.3 Pollutant Transport in the Sewer System.- 2.1.1.4 Biochemical Transformations in the Sewer System.- 2.1.1.5 Storage Tanks.- 2.1.2 The Wastewater Treatment Plant.- 2.1.2.1 Storm Tank.- 2.1.2.2 Primary Clarification.- 2.1.2.3 The Activated Sludge Process.- 2.1.2.4 Secondary Clarification.- 2.1.3 Rivers.- 2.1.3.1 River Flow.- 2.1.3.2 Pollutant Transport in the River.- 2.1.3.3 Biochemical Transformations in the River.- 2.2 Impact of Storm Events on the Urban Wastewater System.- 2.2.1 Impacts on Sewer Systems.- 2.2.2 Impacts on Treatment Plant Performance.- 2.2.3 Impacts on the Receiving River.- 2.2.4 Criteria for the Assessment of River Water Quality.- 2.2.5 The Dilemma of Control of the Urban Wastewater System.- 2.3 Integrated Modelling Approaches.- 2.4 Operational Management of Wastewater Infrastructure.- 2.4.1 General Concepts.- 2.4.2 Real-time Control of Sewer Systems.- 2.4.3 Development of Control Strategies - Exemplified for Sewer Systems.- 2.4.3.1 Off-line Development of Strategies.- 2.4.3.2 On-line Development of Strategies.- 2.4.4 Operation of Wastewater Treatment Plants.- 2.4.5 Real-time Control of Receiving Rivers.- 2.4.6 Integrated Real-time Control.- 2.4.7 Concluding Remarks.- 2.5 Mathematical Optimisation Techniques.- 2.5.1 Definition of the Optimisation Problem.- 2.5.2 A Review of Optimisation Methods.- 2.5.2.1 Local Optimisation.- 2.5.2.2 Global Optimisation.- 2.6 Conclusion.- 3. Development of the Integrated Simulation and Optimisation Tool SYNOPSIS.- 3.1 Requirements on the Simulation Tool.- 3.2 Modules Simulating the Parts of the Urban Wastewater System.- 3.2.1 Implementation of the Sewer System Module.- 3.2.2 Implementation of the Treatment Plant Module.- 3.2.2.1 The Original Implementation of Lessard and Becks Treatment Plant Model.- 3.2.2.2 Modifications of the Treatment Plant Model.- 3.2.3 Implementation of the River Module.- 3.3 Assembling the Integrated Simulation Tool.- 3.3.1 Integration of the Simulation Software.- 3.3.2 Variables in SYNOPSIS.- 3.3.3 Auxiliary Routines Necessary for Simulation.- 3.4 Implementation of Control in SYNOPSIS.- 3.5 Optimisation Algorithms in SYNOPSIS.- 3.5.1 Controlled Random Search.- 3.5.2 A Genetic Algorithm.- 3.5.3 Powells Local Optimisation Method.- 3.5.4 Interfacing the Simulation Tool with the Optimisation Routines.- 3.6 Summary: Overview of the Integrated Simulation and Optimisation Tool SYNOPSIS.- 4. Simulation of the Urban Wastewater System Using SYNOPSIS.- 4.1 Definition of a Case Study Site.- 4.1.1 Existing Data Sets.- 4.1.2 Definition of the Sewer System.- 4.1.3 Definition of the Wastewater Treatment Plant.- 4.1.4 Definition of the River.- 4.1.5 Overview of the Case Study Site Defined.- 4.2 Simulation of Dry-weather Flow.- 4.3 Simulation of a Rainfall Time Series.- 4.4 Analysis of the Control Devices of the Urban Wastewater System.- 4.5 Potential of Reduction in Simulation Time by Selective Simulation.- 4.5.1 Separation of Rainfall Events.- 4.5.2 Potential Savings in Simulation Time.- 4.5.3 Selective Versus Continuous Simulation.- 4.5.4 Conclusions.- 5. Analysis of Control Scenarios by Simulation and Optimisation.- 5.1 Definitions and Methodology.- 5.2 Analysis of Strategy Parameters - an Example.- 5.2.1 Definition of a Strategy Framework.- 5.2.2 Exploring the Parameter Space by Gridding.- 5.2.3 Optimisation of Strategy Parameters.- 5.3 A Top-down Approach to the Definition of Control Strategies.- 5.3.1 Definition of Various Frameworks.- 5.3.2 Evaluation of the Optimisation Algorithms.- 5.3.3 Conclusions.- 5.4 A Bottom-up Approach to the Definition of Control Strategies.- 5.4.1 Towards a Systematic Definition of Frameworks.- 5.4.2 Analysis of Frameworks Involving Several Controllers.- 5.5 Integrated Versus Local Control.- 5.6 Further Aspects.- 5.6.1 Sensitivity of Solutions.- 5.6.2 Multi-objective Optimisation.- 5.6.3 Simulation Period Required for Optimisation.- 5.6.4 Control Potential of Various Case Study Sites.- 6. Conclusions and Further Research.- 6.1 Summary.- 6.2 Suggestions for Further Research.- Appendix A. Overview of Existing Software.- A.1 Software for Simulation of Sewer Systems.- A.2 Software for Simulation of Activated Sludge Wastewater Treatment Plants.- A.3 Software for Simulation of Rivers.- Appendix B. Parameters of the Treatment Plant Model.- Appendix C. Rainfall Data Used in This Study.- Appendix D. Detailed Results of Optimisation Runs Presented in Chapter 5.- References.

Stochastic dynamic programming formulation for a wastewater treatment decision-making framework

In this paper, a decision-making framework (DMF) based on stochastic dynamic programming (SDP) is presented for a wastewater treatment system, consisting of a liquid treatment line with eleven levels and a solid treatment line with six levels (Chen and Beck, 1997). A continuous-state SDP solution approach based on the OA/MARS method (Chen, Ruppert, and Shoemaker, 1999) is employed, which provides an efficient method for representing a wide range of possible influent conditions. The DMF is used to evaluate current and emerging technologies for the multi-level liquid and solid lines of the wastewater treatment system. At each level, one technology unit is selected out of a set of options which includes the empty unit. The DMF provides a comparison on possible technologies for screening which types of technologies may best be further developed in order for an urban wastewater infrastructure to be judged progressively more sustainable. The results indicate that one or a pair of technologies are dominant in each level. The cheap, lower-technology unit processes receive a mixed review. Some of them are selected as the most promising technology units while the others are not considered as good candidates.

Accounting for structural error and uncertainty in a model: An approach based on model parameters as stochastic processes

The significance of model structure error and uncertainty (MSEU), sometimes referred to as conceptual error, is rarely adequately recognized. MSEU, moreover, is not an esoteric matter of little consequence to the formation of policy for environmental protection and ameliorating the prospective effects of climate change. The paper presents an approach to accounting for MSEU in which the parameters of a model are treated as stochastic processes and modeled as Generalized Random Walks. Our approach is inspired by the algorithms of recursive estimation and filtering theory. In particular, given an innovations representation of the models structure, we are able to exploit the dichotomy of what is considered to be the {presumed known} in the models structure and its complement, the {acknowledged unknown}. Two conceptually different groups of model parameters attach to this dichotomy: those familiar to us as the conventional parameters in a models structure; and those having to do with the way in which past (systematic) forecasting errors - in fact, the innovations errors - are distributed (fed back) into the generation of future predictions through a gain matrix (in the sense of filtering theory). A hypothetical biological system with nonlinear dynamics is specified as the prototypical case study for assessing and comparing the performance of our proposed approach with three other approaches to accounting for MSEU: model fitting error; the expansion of parametric uncertainty; and Bayesian model averaging. Our predictive test cases are constructed around future conditions in which the pattern of input disturbances of the systems behavior is broadly similar to that of their past observed pattern (as used for prior identification, or calibration, of the model). In specific terms, however, future input disturbance patterns are significantly different. Our new approach and that of Bayesian model averaging are found to perform well on this hypothetical system; the performances of the approaches of model fitting error and the expansion of parametric uncertainty are shown to be inferior.

A biogeochemical model for metabolism and nutrient cycling in a Southeastern Piedmont impoundment

Abstract While non-point nutrient loads are important determinants of biological productivity in Southeastern Piedmont impoundments, productivity can be attenuated by concomitant sediment loads that reduce the biological availability of these nutrients. A biogeochemical model is proposed that explicitly accounts for the effects of sediment–nutrient interactions on multiple components of phytoplankton metabolism dynamics, including algal photosynthesis and respiration, pH, carbonate speciation, dissolved oxygen, and biochemical oxygen demand. Sediment–nutrient interactions relate nutrient uptake and release to pH, sediment oxygen demand, sediment organic matter, and iron. pH is a state variable in our model, affects sediment–nutrient adsorption, and constrains model parameters. The model replicates water quality observations in a small Southeastern Piedmont impoundment and suggests that pH-dependent sediment–nutrient adsorption dominates both orthophosphate and ammonium dynamics, with phosphate adsorption being controlled by ligand exchange to iron oxides, and ammonium adsorption being controlled by the cation exchange capacity. Sediment organic matter accumulation and decay also affects nutrient availability, and may explain the long-term increase of hypolimnetic dissolved oxygen deficit in Lake Lanier, a large Southeastern Piedmont impoundment.

Design, implementation, and evaluation of an Internet of Things (IoT) network system for restaurant food waste management

Catering companies around the world generate tremendous amounts of waste; those in China are no exception. The paper discusses the design, implementation, and evaluation of a sensor-based Internet of Things (IoT) network technology for improving the management of restaurant food waste (RFW) in the city of Suzhou, China. This IoT-based system encompasses the generation, collection, transportation and final disposal of RFW. The Suzhou case study comprised four steps: (1) examination of the required functionality of an IoT-enabled system in the specific context of Suzhou; (2) configuration of the system architecture, both software and hardware components, according to the identified functionality; (3) installation of the components of the IoT system at the facilities of the stakeholders across the RFW generation-collection-transportation-disposal value chain; and (4) evaluation of the performance of the entire system, based on data from three years of operation. The results show that the system had a strong impact. Positive results include: (1) better management of RFW generation, as evidenced by a 20.5% increase in RFW collected via official channels and a 207% increase in the number of RFW generators under official contract; (2) better law enforcement in response to RFW malpractice, enabled by the monitoring capabilities of the IoT system; and (3) an overall reduction in illicit RFW activities and better process optimization across the RFW value chain. Negative results include: (1) Radio-frequency identification (RFID) tags need to be renewed often due to the frequent handling of waste bins, thus increasing operating costs; (2) dynamic/automatic weight sensors had a higher degree of error than the more time-consuming static/manual weighing method; and (3) there were disagreements between the citys government agencies about how to interpret data from the IoT system, which led to some inefficiencies in management. In sum, the Suzhou IoT system enabled data-driven management of RFW and had a net positive impact for the stakeholders involved.

Cities as Forces for Good in the Environment: A Systems Approach

Background: The various elements of infrastructure in cities and their systems of governance—for transport, buildings, solid waste management, sewerage and wastewater treatment, and so on—may be re-worked such that cities may become forces for good (CFG, for short) in the environment. The chapter is a study in the lessons learned from implementing and pursuing research into how a systems approach can be employed to meet the challenges of achieving CFGs. Methodology: Four case studies in CFG are presented within the framework of the methods and computational models of Systems Dynamics (SD): transport infrastructure for the Kanyakumari city-region in India, resource recovery from wastewater infrastructure in the city of Harare, Zimbabwe, environmental injustice in the handling of solid municipal wastes in Kinshasa, Democratic Republic of Congo, and improving the use of energy in university campus buildings in Bloemfontein, South Africa. Application/Relevance to systems analysis: The chapter presents the successes and the difficulties of undertaking Applied Systems Analysis (ASA) in demanding urban contexts. Policy and practice implications: Policy for CFG derived from ASA often appears to be a matter of determining better technological innovations and engineering interventions in the infrastructure of cities, while practice often demands that infrastructure improvements follow from social and institutional improvements. Conclusion: The first of three conclusions is that combining the rigorous, logical, non-quantitative, more discursive and more incisive style of thinking derived from the humanities, particularly, social anthropology, with better computational modelling will yield better outcomes for ASA. Secondly, in a global context, cities—as opposed to nation-states—are increasingly becoming the locations and scale at which today’s environmental, economic, and social “problems” might best be “solved”. Third, and last, we conclude that South Africa, while it may not have a long tradition of problem-solving according to ASA, has for us emphasised (through our experience of the South African YSSPs) the limitations of an historical over-reliance on hard, quantitative methods of systems analysis.

Simulation of the Urban Wastewater System Using SYNOPSIS

Following the description of the integrated simulation and optimisation tool SYNOPSIS in the previous chapter, this chapter prepares the studies to be carried out in Chapter 5 in various respects. Section 4.1 discusses the availability of data and defines the case study site used throughout this work as well as the relevant input data. Sections 4.2 and 4.3 present results of simulations of dry-weather flow and of a rainfall series, respectively, in order to illustrate the capabilities of the simulation package. Another example of the application of the simulation part of SYNOPSIS is provided in Section 4.4. Here, various settings of some of the control devices available in the urban wastewater system and their impact on receiving water quality are assessed. This prepares the analyses of control strategies in the subsequent chapters. Since the application of optimisation procedures (in Chapter 5) will be potentially demanding in terms of computing time, Section 4.5 analyses to what extent continuous long-term simulations can be substituted by simulations of series of individual events, in order to potentially reduce the time required for simulations.

Not so much the water as what's in it: engineering anthropology for beginners

There is, it is often observed, no waste in nature; waste comes from culture. This means that if there were no human-generated material flows – water, energy, phosphorus, nitrogen, food, carbon dioxide and so on – there would be no waste. But it does not follow from this that the more human-generated flows there are, the more waste there will be. By re-engineering our cities’ infrastructures in ways that enjoy the consent of their citizens – our focus in this paper is on water and its conversion into wastewater – we can progressively alter the material flows from ‘bad’ to ‘good’, with the ultimate goal of making those cities into forces for good in the environment.

Analysis of Control Scenarios by Simulation and Optimisation

This chapter applies the simulation and optimisation tool SYNOPSIS to the evaluation of various control scenarios for the semi-hypothetical case study defined in the previous chapter. Some fundamental defmitions are given in Section 5.1. Section 5.2 provides an example of the application of the simulation and optimisation procedure to the determination of the parameters of a control strategy.