Jul 2026· Journal of Korean Society of Environmental Engineers· Vol 48, pp. 174-188· 0 citations· 48 references
TL;DR
The results suggest that operating control selectively shapes key PHA-associated microorganisms rather than the entire community as a data-driven basis for defining operating windows for MMC-based PHA enrichment and AI-assisted process optimization.
Abstract
Polyhydroxyalkanoate (PHA) is a biodegradable polymer accumulated by microorganisms and is considered a promising alternative to petroleum-based plastics. This study investigated the operating conditions governing PHA accumulation and the enrichment of PHA-accumulating microorganisms in mixed microbial culture (MMC) systems. Data from four sequencing batch reactors (SBRs), operated in two experimental runs with three stages each, were integrated, including 1,733 days of process data, 400 PHA measurements, and 16S rRNA gene amplicon sequencing data from 48 samples. A leakage-free machine learning framework was applied to interpret the relationships among operating conditions, PHA accumulation, and microbial community dynamics. Under reactor-wise cross-validation, XGBoost explained PHA content from operating conditions alone (R2 = 0.38), and SHAP analysis identified settling strategy, nutrient decoupling, solids retention time (SRT), and feast/famine duration as key factors. PHA-accumulating genera, including Thauera, Paracoccus, and Azoarcus, were enriched compared with the inoculum, but their relative abundance was not significantly correlated with measured PHA content (Spearman’s ρ = −0.34, p = 0.06). In contrast, Thauera abundance was predicted from operating conditions in unseen reactors with relatively high accuracy (R2 = 0.61), whereas most other taxa were not predictable. These results suggest that operating control selectively shapes key PHA-associated microorganisms rather than the entire community. This study provides a data-driven basis for defining operating windows for MMC-based PHA enrichment and AI-assisted process optimization.
Polyhydroxyalkanoates (PHAs) are promising biodegradable alternatives to conventional plastics due to their biodegradability and biocompatibility. This study aimed to determine the optimal operational mode for high-content PHA production using acetate-enriched activated sludge and to identify the key microbial populations responsible for efficient PHA synthesis, addressing the feasibility of scaling up sludge-based PHA production.
Activated sludge collected from a local full-scale wastewater treatment plant was acclimated under fully aerobic conditions to enrich PHA-accumulating microorganisms. Acetate was supplied as a low-cost carbon source. Laboratory-scale fermentation experiments (n = 5) were conducted to compare fully aerobic operation with conventional feast and famine strategies. PHA accumulation was quantified, and microbial community composition was analyzed using shotgun metagenomic sequencing.
Fully aerobic conditions with acetate supplementation achieved the highest content of PHA in biomass, reaching 243.2 mg 3HB g
−1
dry weight (367.8 mg 3HB g
−1
VS) after 64 days of fermentation. Unlike conventional feast-and-famine selection strategies, this study demonstrates that high PHA contents can be attained under stable aerobic conditions. The produced PHA consisted primarily of the monomer 3-hydroxybutyrate (3HB) due to the use of acetic acid as an even-carbon acid. Shotgun metagenomic sequencing revealed that the microbial communities were dominated by the phyla Pseudomonadota and Actinomycetota, while the most abundant genus presented during the accumulation of PHA was
Xanthobacter
, known as a PHA producer. This integrated process–microbial insight provides new understanding for optimizing and scaling sludge-based PHA production systems.
The study demonstrates that fully aerobic condition is an effective strategy for enhancing PHA production in acetate-fed activated sludge systems. These findings offer practical insights for scaling up sludge-based PHA production fermentation, contributing to the development of circular bioeconomy approaches in wastewater treatment.
Lei Liu, Taina Lundell, Martin Romantschuk et al.· Waste and Biomass Valorizati...· 0 citations
Accurate prediction and control of chiral lactic acid (LA) production from complex waste streams remain challenging due to nonlinear metabolic interactions and process variability. Unlike previous machine learning (ML) studies that focused solely on predicting total lactic acid (T-LA), this study developed an integrated ML and optimization framework that uses substrate characteristics as input variables to construct predictive models for the two chiral isomers, L-lactic acid (L-LA) and D-lactic acid (D-LA), while also enabling prediction and optimization of T-LA. Supervised models (XGBoost, Gaussian Process Regression, Support Vector Machine, and K-Nearest Neighbors) were combined with unsupervised learning (cluster analysis), global sensitivity analysis (SHAP), and multi-objective optimization using the NSGA-II algorithm. Among the tested models, XGBoost achieved the highest predictive accuracy, with R2 values of 0.90 for L-LA and 0.79 for D-LA during testing, and 0.95-0.98 during independent validation. Sensitivity analysis identified volatile fatty acids (VFA) and soluble chemical oxygen demand (SCOD) as the dominant factors governing isomer production, revealing inhibitory effects at elevated VFA concentrations. Optimization results defined operating regions enabling complete suppression of individual isomers or maximizing L-LA (23 gCOD·L-1) and D-LA (10 gCOD·L-1). In particular, D-LA inhibition was primarily controlled by increasing VFA, whereas L-LA inhibition depended on complex interactions between VFA and substrate composition. It should be noted that the predictive performance of this model depends on the range of the training data, and prediction bias may occur when input variables fall outside the training set's input distribution. Overall, the framework proposed in this study provides a reference for the prediction and decision-making in selectively regulating LA isomers and optimizing the conversion of waste into bioproducts.
B. Szeląg, Yuan Li, Wenjuan Zhang et al.· Journal of Environmental Man...· 0 citations
Polyhydroxyalkanoates (PHA) are fully biodegradable polyesters synthesized by microorganisms from renewable carbon sources, including agro-industrial residues. Soy molasses, a carbohydrate-rich by-product of soybean processing, can be fermented to produce organic acids suitable for PHA production. The established process to produce PHA using mixed microbial cultures (MMC) comprises three stages: acidogenic fermentation, microbial enrichment and PHA accumulation. This study investigated PHA production by MMC through the application of different organic loading rates (OLR) in the enrichment stage in a sequential batch reactor (SBR). The nominal OLR values applied were 2.0, 3.5 and 7.0 kg COD m⁻³ d⁻¹. Among the evaluated conditions, the best measured OLR for selecting PHA-producing MMC was 6.78 ± 0.37 kg COD m⁻³ d⁻¹, based on stability in the enrichment reactor (F/F, 0.15 ± 0.06) and maximum volumetric PHA productivity (0.15 g PHA L⁻¹ h⁻¹) in the accumulation assay. Biomass from the best conditions was also evaluated in an accumulation stage with acidified soy molasses fortified with its main organic acids (lactic, valeric, acetic, and butyric), at a concentration of 79 kg COD m− 3. When using fortified molasses as a substrate, productivity increased to 0.38 g PHA L⁻¹ h⁻¹, with a yield of 0.48 g COD−PHA g COD−dosed⁻¹ (soluble COD), demonstrating a positive effect of high substrate concentration on accumulation performance. The biopolymer produced was a PHBV copolymer, with proportions of 3-hydroxybutyrate (HB) to 3-hydroxyvalerate (HV) ranging from 82:18 to 88:12 (mass basis) under non-fortified conditions, changing to 65:35 (mass basis) when fortified molasses was used.
Andressa Gomes Barroso, Isabela Mehi Gaspari Augusto, J. Rodrigues et al.· Journal of Polymers and the...· 0 citations
The microbial fermentation of mulberry branch residues offers a potential strategy for sustainable lignocellulosic biomass valorization. This study integrated 16S rRNA gene sequencing and untargeted LC–MS metabolomics to characterize temporal microbial and metabolic changes during a 12-day solid-state fermentation at 35 °C using a defined consortium of lactic acid bacteria, Bacillus subtilis, and Saccharomyces cerevisiae. Microbial community analysis revealed distinct temporal succession, with Bacillus accounting for 19.5% of the bacterial community during early fermentation, followed by increasingly diverse assemblages. Exploratory machine learning analysis ranked Azotobacter and Kyrpidia among the genera contributing most strongly to temporal differentiation. FAPROTAX-based predictions indicated that chemoheterotrophy-related functions remained prevalent across fermentation stages, although these predictions do not represent direct functional activity. Untargeted metabolomics detected 2782 putatively annotated features, dominated by lipids (15.1%), organic acids (13.8%), and phenylpropanoids (13.2%). Correlation analysis identified temporal associations between bacterial genera and metabolite classes, including associations of Geobacillus with alkaloids and glycerophospholipids. Exploratory OPLS-DA further highlighted pseudouridine and 3-methylxanthine as discriminatory features across fermentation stages. These findings provide a descriptive multi-omics overview of microbial succession and metabolic variation during mulberry branch residue fermentation.
The fermentation behaviour of Lactobacillus delbrueckii subsp. bulgaricus, Streptococcus thermophilus, and Lactobacillus acidophilus during synbiotic yogurt production enriched with freeze-dried banana powder was investigated in this study with the aim of characterizing microbial interactions, optimizing growth conditions, and developing a machine learning-based tool to predict the fermentation process. Fermentation experiments were performed under controlled temperature (40–43 °C), and prebiotic concentrations (0–30 g·l-1). Machine learning algorithms, including decision tree regression (DTR), random forests (RF), and Gaussian process regression (GPR), were then employed to predict bacterial growth behaviour and identify optimal fermentation conditions. GPR provided an adjusted coefficient of determination (R2adj) value greater than 0.82 for each bacterial strain, making GPR the best machine learning model. Therefore, it was integrated into the software to create a virtual simulation environment capable of representing fermentation dynamics. Based on modelling, the optimum fermentation conditions were determined as 41.5 °C and 20 g·l-1 freeze-dried banana powder concentration. Overall, the findings demonstrate that banana powder boosts probiotic growth, and machine learning enables efficient optimization and monitoring of the synbiotic yogurt fermentation process.
Melissa Şahin, B. Ergüden, Fatih Tarlak· Journal of Food and Nutritio...· 0 citations
Kitchen waste fermentation can provide a low-cost carbon source rich in volatile fatty acids (VFAs) for polyhydroxyalkanoate (PHA) production by mixed microbial cultures (MMCs), while the effects of VFA composition on process performance remain unclear. This study compared the enrichment of PHA-producing microorganisms and PHA production performances of MMCs supplied with acetate-, propionate-, and butyrate-dominant VFAs. Although the three systems achieved similar maximum PHA contents (PHAm) of about 70%, the propionate-type system showed better substrate-switching adaptability and biomass retention, achieving the highest PHA productivity of 0.94 g PHA/(L·d). It indicates that increasing the propionate fraction in the feed may be a promising strategy for MMC-based PHA production. Glycerol-assisted kitchen waste fermentation was therefore investigated to enhance propionate formation. Under the optimized conditions of maintaining pH 6.5 and 35 °C, propionate accounted for approximately 41% of total VFAs. The actual fermentation broth supported a PHA content of 42.5% and a carbon conversion efficiency of 48.3% in the propionate-type system. Further analysis indicated that elevated nutrient and salinity levels were important factors limiting its performance relative to the simulated fermentation broth group. Overall, this study presents a feasible strategy for enhancing MMC-based PHA production through propionate-oriented kitchen waste fermentation.
Xiaolei Zhang, Yingxue Sun, Shaoping Luo et al.· Bioresource Technology· 0 citations
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