Optimized Design and Operation of Activated Sludge Process: Recent Developments
Qiu Yong: Assistant Researcher at School of Environmental Sciences, Tsinghua University, mainly engaged in the research of optimal operation of sewage treatment process, model and simulation of activated sludge system and automatic control technology of sewage treatment process. He has participated in or been in charge of many national key scientific research projects, And made a wealth of academic achievements.
2014 is the 100th anniversary of the activated sludge method, the industry organized many commemorative activities. By reviewing the course of the continuous development of water treatment technology over the past 100 years, we can see that the research results are very rich and the actual application is far from satisfactory. It requires us to resolutely solve the problem when we are striving to explore the frontier. This article recommends a summary of the activated sludge method to optimize the design and operation of the article, hoping to promote everyone's thinking on how the research results fall.
The paper's first and correspondent is RainierHreiz, Department of Chemical Engineering, Lorraine University, France. The authors consider that from an economic and environmental point of view, it is necessary for engineers to use dynamic optimization tools to determine design operating parameters and thus improve the effectiveness of activated sludge processes. However, in practical work, engineers often lack confidence in optimizing results and even real trends. As a result, the author summed up the recent process optimization design results, discussed the above reasons and solutions.
The so-called optimization, is to set the objective function and constraints, through mathematical solution to get the best results. Due to the different economic, operational and environmental operational goals of the wastewater treatment process, the Pareto optimal boundary can be obtained using MOO (Multi-Objective Optimization) or single-objective optimization (SOO) using a weighting factor. The graph below shows the Pareto curve (red) for operating costs and processing effectiveness, which represents the limits of process optimization.
Sewage treatment
Translator's Note: The actual program (blue) optimization goal should be the curve of the AB segment, so that you can get less investment increase the processing efficiency.
For the reason that the calculation results are difficult to be convincing, the author thinks the main reason is that the objective function is inaccurate, the influent characteristics can not be accurately described, and the simulation of complex biological reaction process is not accurate enough. Therefore, the following aspects are discussed.
1, how to consider the difference between model prediction and process data?
Differences between simulation results and process data are common phenomena. For example, ASM and Takacs models are more commonly used, but the parameters are more and more difficult to determine. Therefore, in most cases, only the default parameters can be used. The simulation results at this time can only represent trends and can not be accurately predicted. In order to control this difference, it is usually necessary to calibrate the model parameters with the observed data or determine the parameters to be calibrated by sensitivity analysis (other default values ​​are used).
2, how to deal with unpredictable changes in water features?
Because the dynamic influent optimization results are safer than steady state, it is necessary to observe and determine the temporal variation characteristics of influent water quality. Due to the difficulty of observing and predicting dynamic influent characteristics, long-term averages have to be used as input conditions on many occasions. If steady-state inflow is used to calculate structure parameters, various adverse conditions (such as impact loads) or safety factors need to be checked. In this case, a better approach is to adopt multi-objective optimization.
3, how to determine the controllability of activated sludge process?
Activated sludge process control methods can be broadly divided into open-loop control and closed-loop control. Open-loop control directly calculates the optimal control conditions and actions, while the feedback control compares the actual effect with the control target to adjust. Feedback control to be more reliable, for example, someone studied the online optimization control (RTO), real-time calculation of the current parameters through the online instrument optimization parameters, and then output control adjustments.
4, how to simulate the long-term changes in activated sludge process?
During long-term operation, the characteristics of the activated sludge process may change, so optimization based on short-term or start-up data may not always be suitable for long-term operation. One way to do this is to use long-term data when optimizing and, in addition, simulate multiple times to show the long-term impact of the study.
5, how to determine the preferred process and parameters?
The traditional process selection method is as follows: Constructs and designs many kinds of craft, optimizes the processing effect of the craft, compares and chooses the best craft. Some scholars have proposed a "super structure" method to simplify the above work. The super structure takes the preconditions of the structure (such as residence time, aeration, backflow, etc.) as model optimization parameters. Through the global optimization of these parameters, the optimal treatment effect and the optimal process can be determined at the same time.
Translator's Note: A set of default conditions correspond to a processing technology
6, how to calculate the cost of the process unit process?
Process costs include energy consumption, drug consumption, fines, investment, maintenance and more. Energy consumption accounts for a large proportion, including aeration, lifting and mixing units. The aeration unit is calculated by the mass transfer coefficient of oxygen (KLa). The better way is to estimate the air flow rate. Some studies show that the above two approximate linear, the empirical formula also supports this approximation.
Sludge concentration will affect the proportionality coefficient α, which will greatly affect the energy consumption of aeration. Pump energy consumption is calculated by flow and pressure. If you achieve a complete mixing effect, you need to meet certain mixing input power conditions. Scholars calculated inconsistent results (5, 10 and 14W / m3), but the range can be used as an anorexia / anoxic tank design reference.
Translator's Note: In a fully mixed aerobic tank to install the mixer, to consider the aeration effect of the formation of mixing, blender input power desirable 3W / m3
7, how to choose the constraints to ensure that the process can be realized?
The author summarizes the variable constraints in the process of optimization calculation, including the parameters of effluent quality, MLSS, dissolved oxygen, alkalinity, SRT and reactor size. Such as the structure parameter MLSS is 1000-9000mg / L, dissolved oxygen 2-4mg / L, basicity 6-8mM, SRT is 5-35d and so on. By setting the upper and lower limits of the parameters, to ensure that the output results in engineering can be achieved.
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