Modeling and optimization of sugarcane juice clarification process

Plant-based hydrocolloids for efficient clarification of cane juices: rheological analysis and solidification studies

The present study is designed to study the efficiency of plant-based hydrocolloids for the efficient clarification of sugarcane juice and subsequent production of non-centrifugal sugars (NCSs). NCSs are generally produced with lime or other inorganic solids as a clarification agent, often leading to products with a bland taste and dark color. This work is a first of its kind, where plant-based hydrocolloids such as starch, xanthan gum, and guar gum are used for clarification studies. Clarification efficiency was evaluated in terms of separation efficiency, turbidity removal, sucrose content, color transmittance, and rheology studies. Preliminary studies revealed that starch showed a better separation efficiency of 78% compared to other hydrocolloids, and further rheology studies of starch-clarified juice showed a favourable shear-thickening (dilatant, n = 1.382) behaviour, whereas the other two hydrocolloids showed an unfavourable shear-thinning (pseudo plastic, n < 0.9) behaviour. Eventually, starch was found to be a better clarification agent and is proposed as an alternative to lime-based clarification. Solidification studies were performed with starch at various concentrations (0.02-0.04%), pH (6.8-7.2), and temperature (80 °C-100 °C), and it was found that NCSs produced via starch clarification showed superior properties compared with traditional lime-based clarification processes.

Stakeholder Perspectives on Sensors and Automation in the Louisiana Sugarcane Industry

The sugarcane processing industry in the U.S. state of Louisiana is on the verge of a transformation toward increased automation and real-time data analytics through the adoption of sensor networks. As part of this transition, it was imperative to obtain the current insight and feedback on sensors, automation, and data analytics from sugarcane processors. A short survey was circulated to assess the perspectives of industry stakeholders and seek their feedback on the prime needs for sensors, challenges in incorporating these, and outlook on real-time data analytics and predictive models in processing. Compilation of responses is presented, along with examples of industrially relevant projects that involve sensor networks, real-time data analytics, and artificial intelligence.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12355-023-01261-4.

Biomethane Production from Sugarcane Vinasse in a Circular Economy: Developments and Innovations

Sugarcane ethanol production generates about 360 billion liters of vinasse, a liquid effluent with an average chemical oxygen demand of 46,000 mg/L. Vinasse still contains about 11% of the original energy from sugarcane juice, but this chemical energy is diluted. This residue, usually discarded or applied in fertigation, is a suitable substrate for anaerobic digestion (AD). Although the technology is not yet widespread—only 3% of bioethanol plants used it in Brazil in the past, most discontinuing the process—the research continues. With a biomethane potential ranging from 215 to 324 L of methane produced by kilogram of organic matter in vinasse, AD could improve the energy output of sugarcane biorefineries. At the same time, the residual digestate could still be used as an agricultural amendment or for microalgal production for further stream valorization. This review presents the current technology for ethanol production from sugarcane and describes the state of the art in vinasse AD, including technological trends, through a recent patent evaluation. It also appraises the integration of vinasse AD in an ideal sugarcane biorefinery approach. It finally discusses bottlenecks and presents possible directions for technology development and widespread adoption of this simple yet powerful approach for bioresource recovery.

Handheld near infrared spectrometer and machine learning methods applied to the monitoring of multiple process stages in industrial sugar production

This work aimed to evaluate the performance of a handheld NIR spectrometer in developing calibration models to quantify brix and pol at various stages of an industrial sugar production process. Because of sample variability, collected over two harvesting seasons, NIR measurements were acquired either in transmittance or diffuse reflectance. For modelling purpose, partial least squares (PLS), also combined with variable selection techniques, and support vector machine regression (SVR) were investigated. SVR was applied to handle non-linearities within the data. In general, results illustrated the best performance of SVR, that yielded lower root mean square error of prediction (RMSEP) values for brix and pol for spectra acquisition in transmittance (0.59 and 0.69%w/w) and using diffuse reflectance (1.44 and 2.44%w/w), respectively. Results from models using spectra collected in transmittance were comparable to those reported in other works where benchtop instruments were used, highlighting the cheaper and simpler employment of the portable spectrometer.