Phenomenology of Industrial Recovery: A Scientific Analysis of the Corrected Total Recovery (RTC) in the Brazilian Sugarcane Industry

This positional paper provides a rigorous technical and epistemological analysis of the "Recuperado Total Corrigido" (RTC) as the definitive foundational performance indicator within the Brazilian sugar and ethanol industry. While traditional metrics often fail to account for product mix shifts, the RTC offers a standardized measure of industrial efficiency by normalizing sugar and ethanol yields through stoichiometric equivalencies. Grounded in the epistemology of "correspondence truth" and the laws of thermodynamics, with emphasis in the Second Law, affecting processes and operation, this study investigates the systemic trade-offs inherent in industrial juice treatment. The analysis contrasts the kinetic models of sucrose inversion—governed by Vukov's Equation—with contemporary operational expenditures (OPEX) strategies involving high-performance polymers and precise pH control. It identifies a critical divergence between local accounting savings and systemic energy recovery, resulting in systemic losses in energy recovery due to increased entropy and microbial degradation. The contributions of experts such as Henrique Amorim on fermentation microbiology and Silvio Andrietta on kinetic modeling, are synthesized to argue that excellence in industrial yield is not merely a quantitative goal but a prerequisite for economic sustainability in a global bio-based energy matrix. Furthermore, the study explores how emerging technologies, such as Artificial Intelligence (AI) and Near-Infrared (NIR) spectroscopy, mitigate human cognitive biases in process control, shifting the approach from reactive to predictive management. The paper concludes that adherence to rigorous metrological standards and the scientific method are the only effective safeguards against the mitigation of human cognitive biases through the inexorable increase of entropy in industrial processes. The maximization of RTC is thus presented not merely as a quantitative target, but as a strategic imperative for carbon-neutral competitiveness in the 2024–2026 harvest cycles — and beyond.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-07-28
DOI
https://doi.org/10.5281/zenodo.21633822
Primary Topic
Sugarcane Cultivation and Processing
Type
preprint
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Phenomenology of Industrial Recovery: A Scientific Analysis of the Corrected Total Recovery (RTC) in the Brazilian Sugarcane Industry

Carlos Léger Sherman-Palmer
Zenodo (CERN European Organization for Nuclear Research)
Sugarcane Cultivation and Processing
preprint

Phenomenology of Industrial Recovery: A Scientific Analysis of the Corrected Total Recovery (RTC) in the Brazilian Sugarcane Industry

Carlos Léger Sherman-Palmer
preprint en

Abstract

This positional paper provides a rigorous technical and epistemological analysis of the "Recuperado Total Corrigido" (RTC) as the definitive foundational performance indicator within the Brazilian sugar and ethanol industry. While traditional metrics often fail to account for product mix shifts, the RTC offers a standardized measure of industrial efficiency by normalizing sugar and ethanol yields through stoichiometric equivalencies. Grounded in the epistemology of "correspondence truth" and the laws of thermodynamics, with emphasis in the Second Law, affecting processes and operation, this study investigates the systemic trade-offs inherent in industrial juice treatment. The analysis contrasts the kinetic models of sucrose inversion—governed by Vukov's Equation—with contemporary operational expenditures (OPEX) strategies involving high-performance polymers and precise pH control. It identifies a critical divergence between local accounting savings and systemic energy recovery, resulting in systemic losses in energy recovery due to increased entropy and microbial degradation. The contributions of experts such as Henrique Amorim on fermentation microbiology and Silvio Andrietta on kinetic modeling, are synthesized to argue that excellence in industrial yield is not merely a quantitative goal but a prerequisite for economic sustainability in a global bio-based energy matrix. Furthermore, the study explores how emerging technologies, such as Artificial Intelligence (AI) and Near-Infrared (NIR) spectroscopy, mitigate human cognitive biases in process control, shifting the approach from reactive to predictive management. The paper concludes that adherence to rigorous metrological standards and the scientific method are the only effective safeguards against the mitigation of human cognitive biases through the inexorable increase of entropy in industrial processes. The maximization of RTC is thus presented not merely as a quantitative target, but as a strategic imperative for carbon-neutral competitiveness in the 2024–2026 harvest cycles — and beyond.

Zenodo (CERN European Organization for Nuclear Research)
Université de Bordeaux (FR), Institut des Sciences Cognitives Marc Jeannerod (FR), Cognitive Research (United States) (US)
Sugarcane Cultivation and Processing
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