Autothermal thermophilic aerobic digestion (ATAD) is an advanced sewage sludge treatment which allows compliance with increasingly demanding regulations. Concerning sludge pasteurization, a certain average temperature must be assured in the digester during batch treatment. Aeration flow is the variable most manipulated to regulate the digester temperature. Additionally, the manipulation of the batch sludge flow-which is related to the solid-retention-time-is considered to improve temperature regulation despite variations in air and sludge temperatures and the variability of raw sludge organic content. Thus, a dual-input control structure was provided where the aeration and solid-retention-time contributed as faster and slower inputs, respectively. Two controllers intervened, and the set-point for the batch average temperature was chosen to meet the minimum effluent quality established by the US regulations or European recommendations, considering that lower set point temperatures save aeration costs. A set-point for the aeration allowed us to achieve an extra goal, which aimed at either reducing operation costs or increasing production rates. The two feedback controllers were designed following the robust control methodology known as quantitative feedback theory (QFT). Improvements were compared with single-input (aeration-flow) control strategy and open-loop control strategy. Simulations were performed on a benchmark non-linear simulation model for ATAD.
The aim of this paper is to establish and quantify different operational goals and control strategies in autothermal thermophilic aerobic digestion (ATAD). This technology appears as an alternative to conventional sludge digestion systems. During the batch-mode reaction, high temperatures promote sludge stabilization and pasteurization. The digester temperature is usually the only online, robust, measurable variable. The average temperature can be regulated by manipulating both the air injection and the sludge retention time. An improved performance of diverse biochemical variables can be achieved through proper manipulation of these inputs. However, a better quality of treated sludge usually implies major operating costs or a lower production rate. Thus, quality, production and cost indices are defined to quantify the outcomes of the treatment. Based on these, tradeoff control strategies are proposed and illustrated through some examples. This paper's results are relevant to guide plant operators, to design automatic control systems and to compare or evaluate the control performance on ATAD systems.
<p>Un cuatrirrotor con todo el equipamiento de vuelo se encuentra fijado a una estructura que permite la rotación en el espacio sin desplazamiento. Además, un conjunto de herramientas <em>software</em> desarrolladas con MATLAB-Simulink® ejecutan la programación de su controladora y gestionan la transmisión en tiempo real de consignas y estados del vuelo pilotado remotamente. Para este banco de pruebas se ofrece un simulador que reproduce fielmente el comportamiento del sistema real con el fin de plantear un <em>benchmark</em> de Ingeniería de Control. El problema propuesto es controlar la orientación del mutirrotor definida por los ángulos de Euler. Para ello, deben generarse las tres acciones de control que atacan al sistema de propulsión, considerando las velocidades y ángulos que estima el sistema de navegacion y las consignas angulares. Para lograr un mayor realismo, en las pruebas de comportamiento se pueden modificar la tensión de alimentación, que simula el nivel de carga de la batería, y una acción de control que emula el control de la altura, lo que da lugar a diferentes puntos de operación. El simulador permite configurar experimentos en lazo abierto o cerrado, para tareas de identificación o para analizar el comportamiento de los controladores en diferentes puntos de operación y ante diferentes entradas. El objetivo final es incorporar una ley de control que mejore el comportamiento dado como referencia para cierto experimento. Tras una simulación, una función de evaluación cuantifica las diferencias en el error de seguimiento y en la acción de control entre el control actual y el de referencia para cada grado de libertad. El principal desafío es optimizar el reducido ancho de banda disponible para controlar un sistema dinámico complejo.</p>
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