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<p>En esta investigación se evaluó la gestión de los residuos hospitalarios en una clínica privada de tercer nivel de complejidad de la ciudad de Cali, mediante el uso de una herramienta ponderada por un panel de expertos, basada en las normas colombianas y las recomendaciones de la Organización Mundial de la Salud para la gestión de los residuos hospitalarios. La herramienta evaluó la producción de residuos, la operatividad, capacitación y cumplimiento de compromisos de los involucrados en la gestión. Se diagnosticó previamente la gestión de los residuos en la clínica, se diseñó una herramienta en Excel, y se aplicó mensualmente entre los meses de junio y septiembre de 2013. Los resultados mensuales de la medición facilitaron la identificación de las fallas en la gestión y orientaron la toma de decisiones a los responsables, lo que permitió incrementar la fracción de residuos reciclables en 3%, reducir los residuos comunes en 2,4% y los peligrosos en 0,6%. Se redujo la producción de residuos en la Unidad de Cuidados Intensivos, de 3,87 kg/cama/día en junio, a 3,5 kg/cama/día en septiembre, y en hospitalización de 1,33 kg/cama/día a 1,25 kg/cama/día. La evaluación mensual de la gestión de estos residuos con el uso de la herramienta, permitió la identificación puntual de las fallas en la producción de residuos, el componente operativo, el programa de capacitaciones y el cumplimiento de compromisos por parte de los responsables de la gestión, lo cual entregó información valiosa para apoyar la toma de decisiones en busca de mejorar su desempeño.</p>

This work presents a new standard in the model, identification, and control of monitoring purposes over anaerobic reactors. One requirement that guarantees a normal controller operation is for the faculty to measure the data needed periodically. Due to its inability to easily obtain the concentrations of acidogenic bacteria and methanogenic archaea periodically using reliable and commercial sensors, this paper presents an algorithm composed of an asymptotic observer (considering the reaction rates are unknown), aiming to estimate these concentrations. This method represents a significant advantage because it is possible to perform a resource-saving strategy using standard measurements, such as pH or alkalinity, to calculate them analytically in natural environments. Additionally, two yield parameters were included in the original anaerobic model two (AM2) to unlock implementations for a wide range of organic substrates. The static parameter identification was improved using a new method called step-ahead optimization. It demonstrates significant improvements fitting the mathematical model to data until a 78.7% increase in efficiency (compared with the traditional optimization method genetic algorithm). After the period of convergence, the state observer evidences a small error with a maximum 2% deviation. Finally, numerical simulations demonstrate the structure’s strengths, which constitutes a significant step in paving the way further to implement feasible, cost-effective controls and monitoring systems in the industry.

No abstract

This work presents a nonlinear model predictive control scheme with a novel structure of observers aiming to create a methodology that allows feasible implementations in industrial anaerobic reactors. In this way, a new step-by-step procedure scheme has been proposed and tested by solving two specific drawbacks reported in the literature responsible for the inefficiencies of those systems in real environments. Firstly, the implementation of control structures based on modeling depends on microorganisms’ concentration measurements; the technology that achieves this is not cost-effective nor viable. Secondly, the reaction rates cannot be considered static because, in the extended anaerobic digestion model (EAM2), the large fluctuation of parameters is unavoidable. To face these two drawbacks, the concentration of acidogens and methanogens, and the values of the two reaction rates considered have been estimated by a structure of two observers using data collected by sensors. After 90 days of operation, the error in convergence was lower than 5% for both observers. Four model predictive controller (MPC) configurations are used to test all the previous information trying to maximize the volume of methane and demonstrate a satisfactory operation in a wide range of scenarios. The results demonstrate an increase in efficiency, ranging from 17.4% to 24.4%, using as a reference an open loop configuration. Finally, the operational robustness of the MPC is compared with simulations performed by traditional alternatives used in industry, the proportional-integral-derivative (PID) controllers, where some simple operational scenarios to manage for an MPC are longer sufficient to disrupt a normal operation in a PID controller. For this controller, the simulation shows an error close to the 100% of the reference value.