The use of renewable energy and heat recovery sources (RE&R) in wood drying would help to reduce the environmental footprint of the process and dependence on volatile costs energy sources. The challenge is to adapt drying schedules to fluctuations in energy quantity and temperature. To tackle this challenge, a mechanistic model generates optimized drying conditions. A series of harmonic functions based drying condition generation module has been embedded in the computational model to generate conditions tailored to different RE&R scenarios. The simulated annealing algorithm optimizes energy renewable use. This approach is applied to a kiln dryer equipped with solar PV/T panels for drying thick beech boards. The results suggest original drying conditions that improve the final product quality and double the amount of renewable thermal energy used in the process, but often at the cost of longer drying times and higher operating costs, at least compared to cheap fossil energies.
El siguiente trabajo se propone analizar el modo en que se diseñan y gestionan las políticas de terminalidad educativa en la Ciudad Autónoma de Buenos Aires, con especial foco en el programa a distancia y virtual Adultos 2000. Considerando que la Ley de Educación Nacional N° 26.206 de 2006 ha significado un punto de inflexión en la concepción de la educación como derecho de todas las personas que no caduca con la edad, utilizaremos la noción de accesibilidad para comprender cómo se garantiza el acceso, permanencia y egreso por este tramo de escolaridad a jóvenes y adultos. A través del análisis de diversos datos secundarios, y con el propósito de ofrecer una visión actualizada sobre del tema, se indagará el período 2021-2024 que, a su vez, brindará un mayor entendimiento de la expansión que ha logrado el programa en la pospandemia.
Drying is the most energy consuming process in the industrial transformation of wood. The current energy and climate crisis makes it imperative to adapt the process to energy availability, in terms of quantity, cost and temperature level. Wood drying schedules are historically based on practice. In the present work, a mechanistic drying model is used as a predictive tool to adapt conditions to specific situations. A multiscale computational model, Multi_Wood_DryS, has been combined with a probabilistic optimization code to propose tailor-made drying schedules that meet operators' expectations in terms of energy consumption, quality, drying time and cost. Optimizations of drying schedules of a stack of boards are proposed. The cost-optimized schedule is advantageous for all criteria with a 48% reduction in drying time. The resulting metamodel is a first step toward an intelligent controller for wood drying.
Key message The invasive pine wood nematode is a major threat to pine forests worldwide, causing extensive tree mortality. Although scientific knowledge and control measures are continuously improving, important gaps remain. We argue that some key questions, notably related to early detection and pest management, need to be urgently tackled in countries at risk of invasion such as France.
An X-ray density analyzer was used to determine the evolution of the moisture content (MC) profiles of a wood board during drying. Tests were performed on quartersawn boards of five wood species (oak, beech, Scotch pine, birch, alder) of two thicknesses (20 and 40 mm) dried at low temperature. The water vapor diffusion coefficient and liquid permeability of each wood species were determined by inverse analysis of the profiles using a computational model of coupled heat and mass transfer. These parameters were also measured by classical methods for validation purposes. The inverse method gives values close to the measured ones when using the evolution of the moisture profile, but also quite remarkably when using only the evolution of moisture at the core of the board. On the contrary, the method is less accurate when using only the average kinetics as experimental information.
