CATALYTIC BIOMASS PYROLYSIS IS ONE OF THE MOST PROMISING ROUTES FOR OBTAINING BIO-SUSTAINABLE PRODUCTS THAT REPLACE PETROLEUM DERIVATIVES. THIS STUDY EVALUATES THE PRODUCTION OF AROMATIC COMPOUNDS (BENZENE, TOLUENE, AND XYLENE (BTX)) FROM THE CATALYTIC PYROLYSIS OF LIGNOCELLULOSIC BIOMASS (PINUS RADIATA (PR) AND EUCALYPTUS GLOBULUS (EG)). CHILEAN NATURAL ZEOLITE (NZ) WAS USED AS A CATALYST FOR PYROLYSIS REACTIONS, WHICH WAS MODIFIED BY DOUBLE ION EXCHANGE (H2NZ) AND TRANSITION METALS IMPREGNATION (CU5H2NZ AND NI5H2NZ). THE CATALYSTS WERE CHARACTERIZED BY NITROGEN ADSORPTION, X-RAY DIFFRACTION (XRD), AMMONIUM PROGRAMMED DESORPTION (TPD-NH3), AND SCANNING ELECTRON MICROSCOPY WITH ENERGY DISPERSIVE X-RAY SPECTROSCOPY (SEM-EDS). ANALYTICAL PYROLYSIS COUPLED WITH GAS CHROMATOGRAPHY/MASS SPECTROMETRY (PY-GC/MS) ALLOWED US TO STUDY THE INFLUENCE OF NATURAL AND MODIFIED ZEOLITE CATALYSTS ON BTX PRODUCTION. XRD ANALYSIS CONFIRMED THE PRESENCE OF METAL OXIDES (CUO AND NIO) IN THE ZEOLITE FRAMEWORK, AND SEM-EDS CONFIRMED SUCCESSFUL METAL IMPREGNATION (6.20% FOR CU5H2NZ AND 6.97% FOR NI5H2NZ). PY-GC/MS REVEALED A REDUCTION IN OXYGENATED COMPOUNDS SUCH AS ESTERS, KETONES, AND PHENOLS, ALONG WITH AN INCREASE IN AROMATIC COMPOUNDS IN PR FROM 2.92% W/W (WITHOUT CATALYST) TO 20.89% W/W WITH NI5H2NZ AT A BIOMASS/CATALYST RATIO OF 1/5, AND IN EG FROM 2.69% W/W (WITHOUT CATALYST) TO 30.53% W/W WITH NI5H2NZ AT A BIOMASS/CATALYST RATIO OF 1/2.5. THESE INCREASES CAN BE ATTRIBUTED TO ACIDIC SITES WITHIN THE CATALYST PORES OR ON THEIR SURFACE, FACILITATING DEOXYGENATION REACTIONS SUCH AS DEHYDRATION, DECARBOXYLATION, DECARBONYLATION, ALDOL CONDENSATION, AND AROMATIZATION. OVERALL, THIS STUDY DEMONSTRATED THAT THE CATALYTIC BIOMASS PYROLYSIS PROCESS USING CHILEAN NATURAL ZEOLITE MODIFIED WITH DOUBLE ION EXCHANGE AND IMPREGNATED WITH TRANSITION METALS (CU AND NI) COULD BE HIGHLY ADVANTAGEOUS FOR ACHIEVING SIGNIFICANT CONVERSION OF OXYGENATED COMPOUNDS INTO HYDROCARBONS ...

THIS ARTICLE PRESENTS A NOVEL METHODOLOGY CONDUCTED UNDER CONTROLLED LABORATORY CONDITIONS TO ASSESS THE DYNAMIC BEHAVIOR OF THE COMPONENTS OF RAILWAY TRACKS BY APPLYING AN UNBALANCED MASS EXCITATION FORCE. THE METHODOLOGY FOR OBTAINING ACCURATE MEASUREMENTS, WHICH USES DIFFERENT EXCITATION PARAMETERS, IS BASED ON AN UNBALANCED MASS DEVICE, AND FROM THESE DATA, THE TRANSMISSIBILITY OF THE MASS-ELASTOMER SYSTEM IS ESTIMATED. FOR ASSESSMENT OF THE DYNAMIC BEHAVIOR, DIFFERENT SINE SWEEP RATE EXCITATIONS, THE UNBALANCED MASS, AND BACKGROUND NOISE ARE CONSIDERED. THE EXPERIMENTAL MEASUREMENTS OF TRANSMISSIBILITY WITH A SHAKER AND AN UNBALANCED MASS DEVICE ARE PERFORMED TO VALIDATE THE METHODOLOGY. FOR THIS, FREQUENCY-BY-FREQUENCY TRANSMISSIBILITY MEASUREMENTS AND THE SWEPT SINE WERE PERFORMED BY THE SHAKER, WITH A SINE SWEEP FROM 1 TO 51 HZ, USING THE UNBALANCED MASS DEVICE WITH DIFFERENT SINE SWEEP RATES AND UNBALANCED MASS. THE RESULTS OBTAINED ALLOW COMPARISON OF THE TRANSMISSIBILITY BY EXCITATION AT SPECIFIC FREQUENCIES AND THE SINE SWEEP TO VALIDATE THE EXCITATION PARAMETERS OF THE UNBALANCED MASS DEVICE. THUS, A TRANSMISSIBILITY ESTIMATION ERROR WITH THE SWEEP RATE, THE UNBALANCED MASS, AND THE BACKGROUND NOISE IS DEVELOPED. BY USING THE PROPOSED METHODOLOGY, IT IS POSSIBLE TO LOWER THE ERROR OF THE ESTIMATED TRANSMISSIBILITY OF THE SYSTEM WITH BACKGROUND NOISE.

MACHINE LEARNING IS A TOOL THAT IS BEING USED TO OPTIMIZE HIGHLY COMPLEX INDUSTRIAL PROCESSES. IN THE PLYWOOD PANEL PRODUCTION INDUSTRY, VENEER DRYING IS ONE OF THE MOST IMPORTANT PROCESSES AS IT ALLOWS TO OBTAIN HIGH QUALITY PRODUCTS. THE BIOLOGICAL NATURE AND HIGH STRUCTURAL VARIABILITY OF WOOD TURNS ITS INDUSTRIAL PROCESSING MULTIVARIATE AND DIFFICULT TO CONTROL. THE LARGE NUMBER OF VARIABLES PRESENT AND THE INCREASING POSSIBILITY TO MEASURE THEM IN REAL TIME ARE ENABLING THE AVAILABILITY OF A LARGE AMOUNT OF DATA. NOWADAYS, DATA-DRIVEN APPROACH AND INTELLIGENCE ARTIFICIAL TECHNIQUES, SPECIFICALLY MACHINE LEARNING CAN ENABLE ROBUST PREDICTION AND CONTROL APPROACHES. IN THE PROCESS INDUSTRY, WITH HIGH LEVELS OF AUTOMATION, IT IS POSSIBLE TO ENABLE DECISION MAKING TO PREDICT PRODUCT QUALITY BY MONITORING EXPLANATORY CONTROL VARIABLES. THE OBJECTIVE OF THIS WORK WAS TO EVALUATE A MACHINE LEARNING ALGORITHM CAPABLE OF PREDICTING THE QUALITY OF THE VENEER DRYING PROCESS FROM A CONSIDERABLE NUMBER OF INPUT VARIABLES CAPTURED FROM A REAL INDUSTRIAL PROCESS. THE WEKA PLATFORM AND PYTHON CODE WERE USED. THREE ALGORITHMS WERE EVALUATED: K-NEAREST-NEIGHBOR, EXTREME GRADIENT BOOSTING AND SUPPORT VECTOR MACHINE. VARIABLE AND DIMENSIONALITY REDUCTION, CORRELATION ANALYSIS AND PRINCIPAL COMPONENT ANALYSIS WERE PERFORMED. THE RESULTS SHOWED THAT EXTREME GRADIENT BOOSTING ACHIEVED AN ACCURACY OF 76 % IN PREDICTING QUALITY SCORES. FINALLY, IT IS CONCLUDED THAT BOTH THE DATA ENGINEERING METHODOLOGY AND THE EVALUATED ALGORITHMS WERE EFFICIENT IN PREDICTING INDUSTRIAL DATA.