A new study published in Case Studies in Chemical and Environmental Engineering tackles both problems at once. A research team from Khon Kaen University in Thailand, led by Siriporn Lunprom and Apilak Salakkam, combined a carefully optimized biomass-to-sugar pipeline with a pair of upgraded mathematical models that, for the first time in the Luedeking–Piret framework, explicitly account for the two phenomena that have quietly undermined previous PHB fermentation studies: cell death and the microbial consumption of the very polymer the cells have just made. The work demonstrates that sugarcane bagasse can yield record PHB titers when the process is run in fed-batch mode, and that modified kinetic models can pinpoint exactly when to stop a fermentation to avoid losing product.

The raw material preparation followed a two-stage strategy. First, bagasse was pretreated with alkaline hydrogen peroxide at pH 11.6 and 60 °C for six hours, a treatment that swells and delignifies the fibrous matrix. Composition analysis showed the pretreatment transformed the material from 36.5 percent cellulose, 23.1 percent hemicellulose, and 26.9 percent lignin into a fiber fraction containing 59.1 percent cellulose with lignin reduced to just 8.3 percent. Next, enzymatic saccharification using the commercial cellulase Cellic CTec3 together with a xylanase preparation released a hydrolysate containing 103 grams per liter of reducing sugar, dominated by glucose with a smaller xylose fraction. A fivefold concentrated version of the hydrolysate, containing nearly 499 grams per liter of sugar, served as the feeding solution for later fed-batch experiments.

The producing organism, Paracoccus sp. KKU01, was originally isolated from soil at a cassava starch plant and is known to convert sugars efficiently into PHB. In preliminary shake-flask fermentations on the bagasse hydrolysate, the bacterium grew steadily, consumed glucose over 108 hours, and accumulated up to 5.9 grams per liter of PHB. Interestingly, the xylose in the medium decreased only slowly, and genomic reasoning based on the KEGG database suggested that this strain, like other Paracoccus species, lacks the key enzymes for xylose metabolism, namely xylose isomerase and xylulokinase. The team therefore treated glucose as the true carbon source for growth and polymer synthesis, and confirmed that xylose played no role in either process.

When the process was scaled to a controlled 2-liter bioreactor, performance improved dramatically. The residual cell dry mass climbed rapidly to a peak of 32.9 grams per liter at 36 hours, and PHB production, first detected at 18 hours, reached 8.3 grams per liter by 42 hours — a 1.4-fold higher titer than in flasks, with productivity improving 3.3-fold thanks to precise control of pH, temperature, and dissolved oxygen. But then came the observation that motivated the entire modeling effort: after glucose was exhausted, both biomass and PHB concentrations fell. The polymer, stored intracellularly as a carbon reserve, was being mobilized by the starving cells as an energy source, a natural turnover response that classical fermentation models simply do not describe.

That limitation is rooted in the history of the models themselves. The logistic model, originally devised for human population growth, describes biomass rising smoothly toward a carrying capacity but can never predict a decline. The Luedeking–Piret model, developed in 1959 for lactic acid — a product its producers do not consume — treats product formation as the sum of a growth-associated and a non-growth-associated term and therefore predicts endless accumulation. To fix this, the researchers added a first-order cell death term to the logistic model and a first-order consumption term to the Luedeking–Piret equation, then reformulated both as piecewise change-point models. Growth and PHB accumulation follow classical kinetics until critical times t-crit and t-crit2, after which the models switch to exponential decay governed by a specific death rate and a specific PHB consumption rate. Crucially, the added complexity stays minimal: the AICc criterion confirmed that the extra parameters improved fit rather than chasing noise.

The numbers validated the approach. In the batch bioreactor, the modified logistic model raised the coefficient of determination from 0.9734 to 0.9927 and correctly captured the biomass decline beginning at a predicted critical time of 34.38 hours. The modified Luedeking–Piret model performed even more strikingly, lifting the fit for PHB from a poor 0.6960 to 0.9581 and predicting a PHB peak of 8.34 grams per liter at 41.75 hours with a consumption rate of 0.034 per hour. The fitted parameters also revealed the physiology of the process: the growth-associated coefficient approached zero, meaning essentially all PHB was synthesized during the stationary phase, consistent with nitrogen-limited conditions after the cells stopped dividing.

Fed-batch operation pushed the process further. By feeding concentrated hydrolysate twice, at 36 and 51 hours, whenever glucose dropped below about 15 grams per liter, the team raised the PHB titer to 14.1 grams per liter at 66 hours — roughly 70 percent above the batch result and the highest value yet reported for PHB production from sugarcane bagasse — while yield and productivity held steady at 0.14 grams per gram glucose and 0.21 grams per liter per hour. Again, the modified models proved their worth, tracking the biomass and polymer declines that began after 66 hours, likely driven by the depletion of nutrients other than carbon, since only glucose was being fed. The classical models, by contrast, predicted meaningless continuous accumulation and would have misled any attempt to time the harvest.

To show the framework was not tuned to one system, the team fitted the modified models to published datasets from five unrelated fermentations, including recombinant Ralstonia eutropha growing on glycerol, Bacillus megaterium on bagasse, Cupriavidus necator on corn hydrolysate, and two oleaginous yeasts producing microbial lipids from corn stover, one of them at 1000-liter pilot scale. Across these external systems the models achieved coefficients of determination as high as 0.997 and accuracy factors close to the ideal value of one, correctly identifying decay onsets where they occurred and reporting negligible decay where product levels never fell. This versatility matters because intracellular products such as PHB and microbial lipids share the same fundamental vulnerability: once carbon runs out, the producing cells eat their own reserves.

The practical implications reach beyond bioplastics. Because the modified models estimate the peak product concentration and the exact time it occurs, they offer a parsimonious engineering tool for scheduling harvests, minimizing losses, and designing feeding strategies, all while requiring nothing more than routine biomass and product measurements. The Thai team, supported by Khon Kaen University and the National Science, Research and Innovation Fund, emphasizes that further optimization of polymer content and productivity is still needed before bagasse-derived PHB becomes commercially viable. But the combination of a record titer from an abundant agricultural waste and a modeling framework that finally tells operators when the clock is ticking on their product brings the economics of bacterial plastic one significant step closer to reality.

Subject of Research: Kinetic modeling of PHB bioplastic production from sugarcane bagasse by Paracoccus sp. KKU01

Article Title: Poly(3-hydroxybutyrate) production from sugarcane bagasse by Paracoccus sp.: Kinetic modeling using modified logistic and Luedeking–Piret models incorporating cell death and PHB consumption

Article References: Lunprom, S., Jureemas, N., Komat, P., Moungprayoon, A., Khanpanuek, S., & Salakkam, A. (2026). Poly(3-hydroxybutyrate) production from sugarcane bagasse by Paracoccus sp.: Kinetic modeling using modified logistic and Luedeking–Piret models incorporating cell death and PHB consumption. Case Studies in Chemical and Environmental Engineering, 14, Article 101474. https://doi.org/10.1016/j.cscee.2026.101474

Image Credits: AI Generated

DOI: 10.1016/j.cscee.2026.101474

Keywords: PHB, bioplastic, sugarcane bagasse, Paracoccus sp. KKU01, kinetic modeling, Luedeking-Piret model, logistic model, fed-batch fermentation, PHB mobilization, biorefinery, cell death, lignocellulosic hydrolysate