The self-condensation reaction of acetone, producing diacetone alcohol (DAA), is of great industrial importance. It was used to study the catalytic activity of Mg-Al catalysts synthesized by the coprecipitation method. For this purpose, we synthesized Mg-Al based hydrotalcite with a molar ratio of 3, obtained either after conventional heating or after microwave irradiation with of 100W for three minutes. Structural and chemical properties of the obtained catalysts were characterized, using different techniques: X-ray diffraction (XRD), Fourier transform infrared spectra (FTIR), scanning electron microscope (SEM), equipped with energy dispersive X-ray (EDX), and specific surface area of the catalysts were determined by the methylene blue (MB) adsorption method. Also, these catalysts were tested in the self-condensation reaction of acetone at 273K in the liquid phase without solvent, a reaction which requires very high catalytic activity. The microwave treatment improves the catalyst activity, and the conversion of acetone to diacetone alcohol increases from 13.2 to 18.3 after 8h of reaction. Moreover, the microwave-treated hydrotalcite catalyst, calcined at 723K and rehydrated under a flow of N₂, is the most active and gives conversion of acetone of 52 under the same reaction conditions.

The process of thermal control and regulation in buildings is considered complex. Its complexity lies in the various internal and external physical phenomena impacting its control, and also in the increasingly important requirements of occupant comfort, energy optimization and efficiency, and optimization of measuring and monitoring equipment. Recently, the establishment of technical rules for optimal building thermal control has gained interest in academia and industry. These rules have focused mainly on three aspects: the use of renewable energy, optimal management, and the use of equipment and materials allowing the optimization of energy. However, optimal control has not been addressed enough. In this article, we present a PID controller based on a Neural Network approach for thermal building management and control. The proposed approach is based on two processes: an optimal identification process dedicated to the thermal building behavior prediction impacted by variable and invariable elements, measured and unmeasured factors, and a control process to ensure the desired performance with optimal energy control. The results obtained show the advantages of the adopted system in terms of energy optimization, with an important energy gain of 8% to 11%, along with better regulation and control performance, and in terms of occupant comfort with minimal temperature variations.