Modeling and optimization of microbial lipid fermentation from cellulosic ethanol wastewater by Rhodotorula glutinis based on the support vector machine

Engineering oleaginous red yeasts as versatile chassis for the production of oleochemicals and valuable compounds: Current advances and perspectives

Enabling the transition towards a future circular bioeconomy based on industrial biomanufacturing necessitates the development of efficient and versatile microbial platforms for sustainable chemical and fuel production. Recently, there has been growing interest in engineering non-model microbes as superior biomanufacturing platforms due to their broad substrate range and high resistance to stress conditions. Among these non-conventional microbes, red yeasts belonging to the genus Rhodotorula have emerged as promising industrial chassis for the production of specialty chemicals such as oleochemicals, organic acids, fatty acid derivatives, terpenoids, and other valuable compounds. Advancements in genetic and metabolic engineering techniques, coupled with systems biology analysis, have significantly enhanced the production capacity of red yeasts. These developments have also expanded the range of substrates and products that can be utilized or synthesized by these yeast species. This review comprehensively examines the current efforts and recent progress made in red yeast research. It encompasses the exploration of available substrates, systems analysis using multi-omics data, establishment of genome-scale models, development of efficient molecular tools, identification of genetic elements, and engineering approaches for the production of various industrially relevant bioproducts. Furthermore, strategies to improve substrate conversion and product formation both with systematic and synthetic biology approaches are discussed, along with future directions and perspectives in improving red yeasts as more versatile biotechnological chassis in contributing to a circular bioeconomy. The review aims to provide insights and directions for further research in this rapidly evolving field. Ultimately, harnessing the capabilities of red yeasts will play a crucial role in paving the way towards next-generation sustainable bioeconomy.

Status and future trends in wastewater management strategies using artificial intelligence and machine learning techniques

The two main things needed to fulfill the world's impending need for water in the face of the widespread water crisis are collecting water and recycling. To do this, the present study has placed a greater focus on water management strategies used in a variety of contexts areas. To distribute water effectively, save it, and satisfy water quality requirements for a variety of uses, it is imperative to apply intelligent water management mechanisms while keeping in mind the population density index. The present review unveiled the latest trends in water and wastewater recycling, utilizing several Artificial Intelligence (AI) and machine learning (ML) techniques for distribution, rainfall collection, and control of irrigation models. The data collected for these purposes are unique and comes in different forms. An efficient water management system could be developed with the use of AI, Deep Learning (DL), and the Internet of Things (IoT) structure. This study has investigated several water management methodologies using AI, DL and IoT with case studies and sample statistical assessment, to provide an efficient framework for water management.

Artificial neural network and genetic algorithm coupled fermentation kinetics to regulate L-lysine fermentation

Fermentation plays a pivotal role in the industrialization of bioproducts, yet there is a substantial lag in the fermentation process regulation. Here, an artificial neural network (ANN) and genetic algorithm (GA) coupled with fermentation kinetics were employed to establish an innovative lysine fermentation control. Firstly, the strategy of coupling GA with ANN was established. Secondly, specific lysine formation rate (qp), specific substrate consumption rate (qs), and specific cell growth rate (μ) were predicted and optimized by ANN-GA. The optimal ANN model adopts a three-layer feed-forward back-propagation structure (4:10:1). The optimal fermentation control parameters are obtained through GA. Finally, when the carbon to nitrogen ratio, residual sugar concentration, ammonia nitrogen concentration, and dissolved oxygen were [2.5, 4.5], [6.5, 9.5] g·L-1, [1.0, 2.0] g·L-1 and [20, 30] %, respectively, the lysine concentration reaches its peak at 213.0 ± 5.10 g·L-1. The novel control strategy holds significant potential for optimizing the fermentation of other bioproducts.

Identifying major depressive disorder with associated sleep disturbances through fMRI regional homogeneity at rest

BACKGROUND

Anomalies in regional homogeneity (ReHo) have been documented in patients with major depressive disorder (MDD) and sleep disturbances (SDs). This investigation aimed to scrutinize changes in ReHo in MDD patients with comorbid SD, and to devise potential diagnostic biomarkers for detecting sleep-related conditions in patients with MDD.

METHODS

Patients with MDD and healthy controls underwent resting-state functional magnetic resonance imaging scans. SD severity was quantified using the 17-item Hamilton Rating Scale for Depression. Subsequent to the acquisition of imaging data, ReHo analysis was performed, and a support vector machine (SVM) method was employed to assess the utility of ReHo in discriminating MDD patients with SD.

RESULTS

Compared with MDD patients without SD, MDD patients with SD exhibited increased ReHo values in the right posterior cingulate cortex (PCC)/precuneus, right median cingulate cortex, left postcentral gyrus (postCG), and right inferior temporal gyrus (ITG). Furthermore, the ReHo values in the right PCC/precuneus and ITG displayed a positive correlation with clinical symptoms across all patients. SVM classification results showed that a combination of abnormal ReHo in the left postCG and right ITG achieved an overall accuracy of 84.21%, a sensitivity of 81.82%, and a specificity of 87.50% in identifying MDD patients with SD from those without SD.

CONCLUSION

We identified disrupted ReHo patterns in MDD patients with SD, and presented a prospective neuroimaging-based diagnostic biomarker for these patients.

Novel slow-release carbon source improves anodic denitrification and electricity generation efficiency in microbial fuel cells

MFC anodic denitrification is more suitable for the coexistence of organic matter and nitrate in actual sewage, but the traditional carbon source has some problems such as high cost and difficulty of dosage control in MFC. Herein, corncob and polycaprolactone (PCL) were mechanically pulverized and mixed in the system of polyvinyl alcohol and sodium alginate, and cross-linked to prepare slow-release carbon source fillers (CPSP), which were added to the MFC anolyte to realize the coupling of solid-phase denitrification and anodic denitrification. Results showed the start-up period of MFC experimental group (MFC-C) with CPSP was slightly longer than the control group (MFC-0), but MFC-C's maximum output voltage (648.4 mV) and power density (2738 mW/m3) could be increased by 5% and 15% higher than that of MFC-0 (P < 0.05). The degradation process of MFC substrate in unit cycle was mainly divided into nitrogen removal stage (0-8 h) and electricity generation stage (8-48 h). The NO3--N and COD degradation and power generation kinetic processes of MFC conformed to the Han-Levenspiel model. Kinetics experiments showed CPSP can improve the affinity and tolerance of MFC to NO3--N, also it can alleviate the pressure of electron competition in anolyte and improve coulombic efficiency. In addition, microbial communities were significantly changed under the effect of CPSP (P < 0.001). Meanwhile, CPSP can promote the synthesis of denitrification functional genes. This study provides a new strategy to improve the performance of MFC by the addition of novel denitrification carbon source.

Developing high-dimensional machine learning models to improve generalization ability and overcome data insufficiency for mixed sugar fermentation simulation

Biorefinery can be promoted by building accurate machine learning models. This work proposed a strategy to enhance model's generalization ability and overcome insufficient data conditions for mixed sugar fermentation simulation. Multiple inputs single output models, using initial glucose, initial xylose, and time together as inputs, have higher generalization ability than single input single output models with time as sole input in predicting glucose, xylose, ethanol, or biomass separately. Multiple inputs multiple outputs models, integrating outputs, enhanced model accuracy and resulted in an average R² at 0.99. To overcome data insufficiency conditions, consensus yeast (CY) model, through consolidating data from 4 yeasts, obtained R² at 0.90. By adjusting the pretrained CY model, the model can save more than 50% data and get R² at 0.95 and 0.93 for yeast and bacterial fermentation simulation. The strategy can expand the application range and save costs of data curation for ANN models.

From Spatial-Temporal Multiscale Modeling to Application: Bridging the Valley of Death in Industrial Biotechnology

The Valley of Death confronts industrial biotechnology with a significant challenge to the commercialization of products. Fortunately, with the integration of computation, automation and artificial intelligence (AI) technology, the industrial biotechnology accelerates to cross the Valley of Death. The Fourth Industrial Revolution (Industry 4.0) has spurred advanced development of intelligent biomanufacturing, which has evolved the industrial structures in line with the worldwide trend. To achieve this, intelligent biomanufacturing can be structured into three main parts that comprise digitalization, modeling and intellectualization, with modeling forming a crucial link between the other two components. This paper provides an overview of mechanistic models, data-driven models and their applications in bioprocess development. We provide a detailed elaboration of the hybrid model and its applications in bioprocess engineering, including strain design, process control and optimization, as well as bioreactor scale-up. Finally, the challenges and opportunities of biomanufacturing towards Industry 4.0 are also discussed.

Machine learning in bioprocess development: from promise to practice

Fostered by novel analytical techniques, digitalization, and automation, modern bioprocess development provides large amounts of heterogeneous experimental data, containing valuable process information. In this context, data-driven methods like machine learning (ML) approaches have great potential to rationally explore large design spaces while exploiting experimental facilities most efficiently. Herein we demonstrate how ML methods have been applied so far in bioprocess development, especially in strain engineering and selection, bioprocess optimization, scale-up, monitoring, and control of bioprocesses. For each topic, we will highlight successful application cases, current challenges, and point out domains that can potentially benefit from technology transfer and further progress in the field of ML.

Machine learning and circular bioeconomy: Building new resource efficiency from diverse waste streams

Biorefinery systems are playing pivotal roles in the technological support of resource efficiency for circular bioeconomy. Meanwhile, artificial intelligence presents great potential in handling scientific tasks of high-dimensional complexity. This review article scrutinizes the status of machine learning (ML) applications in four critical biorefinery systems (i.e. composting, fermentation, anaerobic digestion, and thermochemical conversions) as well as their advancements against traditional modeling techniques of mechanistic approach. The contents cover their algorithm selections, modeling challenges, and prospective improvements. Perspectives are sketched to further inform collective efforts on crucial aspects. The multidisciplinary interchange of modeling knowledge will enable a more progressive digital transformation of sustainability efforts in supporting sustainable development goals.

Artificial intelligence and machine learning-based monitoring and design of biological wastewater treatment systems

Artificial intelligence (AI) and machine learning (ML) are currently used in several areas. The applications of AI and ML based models are also reported for monitoring and design of biological wastewater treatment systems (WWTS). The available information is reviewed and presented in terms of bibliometric analysis, model's description, specific applications, and major findings for investigated WWTS. Among the applied models, artificial neural network (ANN), fuzzy logic (FL) algorithms, random forest (RF), and long short-term memory (LSTM) were predominantly used in the biological wastewater treatment. These models are tested by predictive control of effluent parameters such as biological oxygen demand (BOD), chemical oxygen demand (COD), nutrient parameters, solids, and metallic substances. Following model performance indicators were mainly used for the accuracy analysis in most of the studies: root mean squared error (RMSE), mean square error (MSE), and determination coefficient (DC). Besides, outcomes of various models are also summarized in this study.

Artificial intelligence technologies in bioprocess: Opportunities and challenges

Bioprocess control and optimization are crucial for tapping the metabolic potential of microorganisms, and which have made great progress in the past decades. Combination of the current control and optimization technologies with the latest computer-based strategies will be a worth expecting way to improve bioprocess further. Recently, artificial intelligence (AI) emerged as a data-driven technique independent of the complex interactions used in mathematical models and has been gradually applied in bioprocess. In this review, firstly, AI-guided modeling approaches of bioprocess are discussed, which are widely applied to optimize critical process parameters (CPPs). Then, AI-assisted rapid detection and monitoring technologies employed in bioprocess are summarized. Next, control strategies according to the above two technologies in bioprocess are analyzed. Lastly, current research gaps and future perspectives on AI-guided optimization and control technologies are discussed. This review provides theoretical guidance for developing AI-guided bioprocess optimization and control technologies.

Optimization and Scale-Up of Fermentation Processes Driven by Models

In the era of sustainable development, the use of cell factories to produce various compounds by fermentation has attracted extensive attention; however, industrial fermentation requires not only efficient production strains, but also suitable extracellular conditions and medium components, as well as scaling-up. In this regard, the use of biological models has received much attention, and this review will provide guidance for the rapid selection of biological models. This paper first introduces two mechanistic modeling methods, kinetic modeling and constraint-based modeling (CBM), and generalizes their applications in practice. Next, we review data-driven modeling based on machine learning (ML), and highlight the application scope of different learning algorithms. The combined use of ML and CBM for constructing hybrid models is further discussed. At the end, we also discuss the recent strategies for predicting bioreactor scale-up and culture behavior through a combination of biological models and computational fluid dynamics (CFD) models.

Abnormal regional homogeneity in right caudate as a potential neuroimaging biomarker for mild cognitive impairment: A resting-state fMRI study and support vector machine analysis

Objective

Mild cognitive impairment (MCI) is a heterogeneous syndrome characterized by cognitive impairment on neurocognitive tests but accompanied by relatively intact daily activities. Due to high variation and no objective methods for diagnosing and treating MCI, guidance on neuroimaging is needed. The study has explored the neuroimaging biomarkers using the support vector machine (SVM) method to predict MCI.

Methods

In total, 53 patients with MCI and 68 healthy controls were involved in scanning resting-state functional magnetic resonance imaging (rs-fMRI). Neurocognitive testing and Structured Clinical Interview, such as Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog) test, Activity of Daily Living (ADL) Scale, Hachinski Ischemic Score (HIS), Clinical Dementia Rating (CDR), Montreal Cognitive Assessment (MoCA), and Hamilton Rating Scale for Depression (HRSD), were utilized to assess participants' cognitive state. Neuroimaging data were analyzed with the regional homogeneity (ReHo) and SVM methods.

Results

Compared with healthy comparisons (HCs), ReHo of patients with MCI was decreased in the right caudate. In addition, the SVM classification achieved an overall accuracy of 68.6%, sensitivity of 62.26%, and specificity of 58.82%.

Conclusion

The results suggest that abnormal neural activity in the right cerebrum may play a vital role in the pathophysiological process of MCI. Moreover, the ReHo in the right caudate may serve as a neuroimaging biomarker for MCI, which can provide objective guidance on diagnosing and managing MCI in the future.

Comparative Analysis of Support Vector Machine Regression and Gaussian Process Regression in Modeling Hydrogen Production from Waste Effluent

Organic-rich substrates from organic waste effluents are ideal sources for hydrogen production based on the circular economy concept. In this study, a data-driven approach was employed in modeling hydrogen production from palm oil mill effluents and activated sludge waste. Seven models built on support vector machine (SVM) and Gaussian process regression (GPR) were employed for the modeling of the hydrogen production from the waste sources. The SVM was incorporated with linear kernel function (LSVM), quadratic kernel function (QSVM), cubic kernel function (CSVM), and Gaussian fine kernel function (GFSVM). While the GPR was incorporated with the rotational quadratic kernel function (RQGPR), squared exponential kernel function (SEGPR), and exponential kernel function (EGPR). The model performance revealed that the SVM-based models did not show impressive performance in modeling the hydrogen production from the palm oil mill effluent, as indicated by the R2 of −0.01, 0.150, and 0.143 for LSVM, QSVM, and CSVM, respectively. Similarly, the SVM-based models did not perform well in modeling the hydrogen production from activated sludge, as evidenced by R2 values of 0.040, 0.190, and 0.340 for LSVM, QSVM, and CSVM, respectively. On the contrary, the SEGPR, RQGPR, SEGPR, and EGPR models displayed outstanding performance in modeling the prediction of hydrogen production from both oil palm mill effluent and activated sludge, with over 90% of the datasets explaining the variation in the model output. With the R2 > 0.9, the predicted hydrogen production was consistent with the SEGPR, RQGPR, SEGPR, and EGPR with minimized prediction errors. The level of importance analysis revealed that all the input parameters are relevant in the production of hydrogen. However, the influent chemical oxygen demand (COD) concentration and the medium temperature significantly influenced the hydrogen production from palm oil mill effluent, whereas the pH of the medium and the temperature significantly influenced the hydrogen production from the activated sludge.

Machine learning exhibited excellent advantages in the performance simulation and prediction of free water surface constructed wetlands

Optimizing the design and operation parameters of free water surface constructed wetlands (FWS CWs) in runoff regulation and wastewater treatment is necessary to improve the comprehensive performance. In this study, nine machine learning (ML) algorithms were successfully developed to optimize the parameter combinations for FWS CWs. The scale effect of surface area on wetland performance was determined based on consistently smaller predictions (-6.2% to -28.9%) of the nine well-established ML algorithms. The models most suitable for FWS CW performance simulation and prediction were random forest and extra trees algorithms because of their high R² values (0.818 in both) with the training set and low mean absolute relative errors (4.7% and 3.8%, respectively) with the test set. Results from feature analysis of the six tree-based algorithms emphasized the importance of water depth and layout of inlet and outlet, and revealed the negligible effect of the aspect ratio. Feature importance and partial dependence analysis enhanced the interpretability of the tree-based algorithms. The proposed ML algorithms enabled the implementation of an extended scenario at a low cost in real time. Therefore, ML algorithms are suitable for expressing the complex and uncertain effects of the design and operation parameters on the performance of FWS CWs. Acquiring datasets consisting of more extensive, uniform, and unbiased parameter combinations is crucial for developing more robust and practical ML algorithms for the optimal design of FWS CWs.

Machine learning aided experimental approach for evaluating the growth kinetics of Candida antarctica for lipase production

A hybrid machine learning (ML) aided experimental approach was proposed in this study to evaluate the growth kinetics of Candida antarctica for lipase production. Different ML models were trained and optimized to predict the growth curves at various substrate concentrations. Results on comparison demonstrate the superior performance of the Gradient boosting regression (GBR) model in growth curves prediction. GBR-based growth kinetics was found to be matching well with the results of the conventional experimental approach while significantly reducing the experimental effort, time, and resources by ∼ 50%. Further, the activity and enzyme kinetics of lipase produced in this study was investigated on hydrolysis of p-nitrophenyl butyrate resulting in a maximum lipase activity of 24.07 U at 44 h. The robustness and significance of developed kinetic models were ensured through detailed statistical analysis. The application of the proposed hybrid approach can be extended to any other microbial process.

Prediction of heterogeneous Fenton process in treatment of melanoidin-containing wastewater using data-based models

Predictive capability of response surface methodology (RSM) and ant colony optimization combined with support vector regression (ACO-SVR) models are applied for determining optimal parameters in the process of heterogeneous Fenton oxidation of melanoidin, a high molecular weight polymer widely produced during fermentation processes generating large quantities of wastewater with intense brown color and extremely high chemical oxygen demand (COD). Prediction of the performance of nano zero-valent iron supported on activated carbon cloth-chitosan (ACC-CH-nZVI) catalysts was carried out using Box-Behnken design (BBD) and analysis of variance to evaluate the interaction of independent variables involved in heterogeneous Fenton reaction. The optimized condition with minimal consumption of H₂O₂ (173 mM) resulted in 77.1% decolorization of melanoidin-contaminated water corresponding to 74.4% COD removal at pH 3 (600 mg/l Fe dosage) for 90 min reaction time. The corresponding weight ratio of H₂O₂ to COD was 0.98, much lower than the stoichiometric value 2.125, indicating the effectiveness of ACC-CH-nZVI as a heterogeneous Fenton-like catalyst. In comparison to previously published experimental results, ACO-SVR model shows higher coefficient of determination (R²; 0.9983) but lower root mean squared error (RMSE) and mean absolute error (MAE) than those of RSM model, indicating relative superiority in prediction capability. Besides, ACO algorithm appears to be a promising tool for improving forecasting accuracy of SVR model. This work demonstrates the applicability of ACO-SVR model in predicting the performance of wastewater treatment using Fenton process with limited number of experiment and exhibits satisfactory prediction results.

Increased Lipid Production in Yarrowia lipolytica from Acetate through Metabolic Engineering and Cosubstrate Fermentation

Bioconversion of acetate, a byproduct generated in industrial processes, into microbial lipids using oleaginous yeasts offers a promising alternative for the economic utilization of acetate-containing waste streams. However, high acetate concentrations will inhibit microbial growth and metabolism. In this study, the acetate utilization capability of Yarrowia lipolytica PO1f was successively improved by overexpressing the key enzyme of acetyl-CoA synthetase (ACS), which resulted in an accumulation of 9.2% microbial lipids from acetate in shake flask fermentation. By further overexpressing the second key enzymes of acetyl-CoA carboxylase (ACC1) and fatty acid synthase (FAS) in Y. lipolytica, the lipid content was increased to 25.7% from acetate. Finally, the maximum OD₆₀₀ of 29.2 and a lipid content of 41.7% were obtained with the engineered strain by the adoption of cosubstrate (glycerol and acetate) fed-batch fermentation, which corresponded to an increase of 68 and 95%, respectively. These results presented a promising strategy for economic and efficient microbial lipid production from the waste acetate.

Culturing rhodotorula glutinis in fermentation-friendly deep eutectic solvent extraction liquor of lignin for producing microbial lipid

Currently, deep eutectic solvents (DES) have attracted increasing attention due to their excellent performance in delignification. However, few studies focused on the treatment of DES waste liquid after extraction of lignin. In this work, the fermentation-friendly DES comprised of glycerol, choline chloride (ChCl) and acetic acid (AA) was applied for delignification of lignocellulose. Subsequently, the extraction effects of different DES were investigated, and the DES extraction liquor was used for lipid production. Results shows ChCl made little difference to lipid synthesis, while excessive AA exerted inhibitory effect on the growth of cells. Following pretreatment, the delignification exceeded 63%. When the DES liquid obtained after lignin extraction was used to produce lipid, the delay period was obvious, while the lipid yield and content were unaffected. Not only is the DES prepared in this study effective in delignification of lignocellulose, it is also applicable as raw material to produce lipid.

Synergisms of machine learning and constraint-based modeling of metabolism for analysis and optimization of fermentation parameters

Recent noteworthy advances in the development of high-performing microbial and mammalian strains have enabled the sustainable production of bio-economically valuable substances such as bio-compounds, biofuels, and biopharmaceuticals. However, to obtain an industrially viable mass-production scheme, much time and effort are required. The robust and rational design of fermentation processes requires analysis and optimization of different extracellular conditions and medium components, which have a massive effect on growth and productivity. In this regard, knowledge- and data-driven modeling methods have received much attention. Constraint-based modeling (CBM) is a knowledge-driven mathematical approach that has been widely used in fermentation analysis and optimization due to its capabilities of predicting the cellular phenotype from genotype through high-throughput means. On the other hand, machine learning (ML) is a data-driven statistical method that identifies the data patterns within sophisticated biological systems and processes, where there is inadequate knowledge to represent underlying mechanisms. Furthermore, ML models are becoming a viable complement to constraint-based models in a reciprocal manner when one is used as a pre-step of another. As a result, more predictable model is produced. This review highlights the applications of CBM and ML independently and the combination of these two approaches for analyzing and optimizing fermentation parameters. This article is protected by copyright. All rights reserved.

Enhanced lipid production by addition of malic acid in fermentation of recombinant Mucor circinelloides Mc-MT-2

In our previous work, we reported a novel approach for increasing lipid production in an oleaginous fungus Mucor circinelloides by overexpression of mitochondrial malate transporter protein. This transporter plays a vital role in fatty acid biosynthesis during malate and citrate transport systems in oleaginous fungi. In this study, the controlling metabolic supplementation strategy was used to improve the lipid production by overexpression of malate transporter protein in M. circinelloides strain coded as Mc-MT-2. The effects of different metabolic intermediates on lipid production in batch fermentation by Mc-MT-2 were investigated. The optimal lipid production was obtained at 0.8% malic acid after 24 h of fermentation. Furthermore, in fed-batch bioreactors containing glucose as a carbon source supplemented with malic acid, the highest cell growth, and lipid production were achieved. The resulting strain showed the fungal dry biomass of 16 g/L, a lipid content of 32%, lipid yield of 5.12 g/L in a controlled bench-top bioreactor, with 1.60-, 1.60- and 2.56-fold improvement, respectively, compared with the batch control without supplementation of malic acid. Our findings revealed that the addition of malic acid during fermentation might play an important role in lipid accumulation in the recombinant M. circinelloides Mc-MT-2. This study provides valuable insights for enhanced microbial lipid production through metabolic supplementation strategy in large scale and industrial applications.

Modelling and process optimization for biodiesel production from Nannochloropsis salina using artificial neural network

In the present investigation, calcium oxide solid nanocatalyst derived from the egg shell and Nannochloropsis salina were used for the production of biodiesel. The morphological characteristics and functional groups of synthesized nanocatalyst was characterized by SEM and FTIR analysis. Process variables optimization for biodiesel production was studied using RSM and ANN. The R² values for RSM and ANN was found to be 0.8751 and 0.957 which showed that the model was significantly fit with the experimental data. The maximum FAME conversion for the synthesized nanocatalyst CaO was found to be 86.1% under optimum process conditions (nanocatalyst amount: 3% (w/v); oil to methanol ratio 1:6 (v/v); reaction temperature: 60 °C; reaction time 55 min). Concentration of FAME present in biodiesel was identified by GC-MS analysis.

Comparative study of lipid production from cellulosic ethanol fermentation wastewaters by four oleaginous yeasts

The biochemical treatment of cellulosic ethanol wastewater (CEW) is considered to be an ideal green process. To screen out the best oleaginous yeastfor the utilization of cellulosic ethanol wastewater, four oleaginous yeasts (Trichosporon cutaneum, Rhorosporidium toruloides, Cryptococcus albidus and T. coremiiforme) were compared to assess their abilities for lipid production in terms of biomass production, lipid content and lipid yield. Furthermore, thechemical oxygen demand (COD) conversion rate, COD degradation and fatty acid composition were calculated to analyze the effect of wastewaters treatment. According to the fermentation results, the highest biomass and lipid yield of T. cutaneum in CEW were 20.945 and 1.56 g/L, respectively, while the R. toruloides reached the highest lipid content (17.32%). The maximum conversion rates of T. cutaneum are 73.64 and 6.06%, respectively, in terms of conversion yield of biomass/COD and lipids/COD. The content of fatty acids showed that after six days' fermentation, T. coremiiforme obtained the highest unsaturated fatty acid content, the content of C18:1 and C18:2 was 57.64%. This study suggests that T. cutaneum has great potential for lipid production and wastewaters treatment from cellulosic ethanol fermentation.