Natural fibres have been attracted significant interest due to its renewable nature, its vast source of raw material, besides having good mechanical properties and low cost. The cellulose present in cell walls of plants can be purified and isolated, acting as a reinforcing agent in polymer composites, in order to obtain lighter, resistant and biodegradable composites. The techniques of acid hydrolysis are methods used to isolate crystals of cellulose, using different acids, resulting in different materials. In this study it was possible to verify the effect of the acid hydrolysis process, with hydrochloric acid (HCl) and sulfuric acid (H 2 SO 4 ), resulting in MCC and NCC by one hydrolysis step, and then were analysed for its chemical and morphological characteristics, presenting significant improvements on these properties.

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Carbon dioxide conversion processes are promising alternatives to reduce its industrial emissions. Most innovative processes are still at a low technological readiness level, and thermodynamic assessments are essential for their development. Thus, this work briefly describes the main aspects of thermodynamic analysis and its application for evaluating specific issues of carbon dioxide conversion reactions. The thermodynamic chemical equilibrium is briefly described. Several carbon dioxide conversion reactions to higher-value products are studied, regarding their thermochemistry and influences of temperature, pressure, and solvent (gas and liquid). Thermodynamic analyses are applied to two case studies of carbon dioxide conversion reactions to formic and acetic acids. High pressures, low temperatures, absence of a gas solvent, and abundant suitable liquids likely enhance the carbon dioxide conversion to the most promising products.

The accelerated use of Artificial Neural Networks (ANNs) in Chemical and Process Engineering has drawn the attention of scientific and industrial communities, mainly due to the Big Data boom related to the analysis and interpretation of large data volumes required by Industry 4.0. ANNs are well-known nonlinear regression algorithms in the Machine Learning field for classification and prediction and are based on the human brain behavior, which learns tasks from experience through interconnected neurons. This empirical method can widely replace traditional complex phenomenological models based on nonlinear conservation equations, leading to a smaller computational effort – a very peculiar feature for its use in process optimization and control. Thereby, this chapter aims to exhibit several ANN modeling applications to different Chemical and Process Engineering areas, such as thermodynamics, kinetics and catalysis, process analysis and optimization, process safety and control, among others. This review study shows the increasing use of ANNs in the area, helping to understand and to explore process data aspects for future research.