Effect of organic carbon sources and environmental factors on cell growth and lipid content of Pavlova lutheri

The present study aimed to investigate the effects of organic carbon sources, cultivation methods, and environmental factors on growth and lipid content of Pavlova lutheri for biodiesel production. In the 250-mL flask bioreactors, P. lutheri was cultivated in the modified artificial seawater (ASW) medium containing glucose, glycerol, sodium acetate, or sucrose as an organic carbon substrate. The effects of different growth conditions (phototrophic, mixotrophic, and heterotrophic) and environmental factors such as photoperiod, light intensity, and salinity were evaluated. Growth of P. lutheri was inhibited under heterotrophy but was enhanced in mixotrophy as compared to phototrophy. Biomass and lipid content of P. lutheri were significantly (p < 0.05) affected by changing photoperiod, light intensity, and salinity. Higher biomass concentration and lipid content were observed at a light intensity of 100 ± 2 μmol photons m−2 s−1, 18 h photoperiod, and 30% salinity, in a modified ASW medium supplemented with 10 mmol sucrose. An increase in biomass concentration from 320 ± 25.53 to 1106 ± 18.52 mg L−1 and high lipid content of 31.11 ± 1.65% (w/w) were observed with the optimized culture conditions, demonstrating a significant (p < 0.05) enhancement in biomass and lipid content due to the improved culture conditions. The present study emphasizes the possible use of sucrose for biomass and lipid production with P. lutheri under the optimized culture conditions. Using low-cost and relatively easy accessible feedstock such as sucrose would be a valuable alternative for growing microalgae with enhanced lipid content.     

Culture of microalgae Chlorella minutissima for biodiesel feedstock production

Microalgae are among the most promising of non‐food based biomass fuel feedstock alternatives. Algal biofuels production is challenged by limited oil content, growth rate, and economical cultivation. To develop the optimum cultivation conditions for increasing biofuels feedstock production, the effect of light source, light intensity, photoperiod, and nitrogen starvation on the growth rate, cell density, and lipid content of Chlorella minutissima were studied. The fatty acid content and composition of Chlorella minutissima were also investigated under the above conditions. Fluorescent lights were more effective than red or white light‐emitting diodes for algal growth. Increasing light intensity resulted in more rapid algal growth, while increasing the period of light also significantly increased biomass productivity. Our results showed that the lipid and triacylglycerol content were increased under N starvation conditions. Thus, a two‐phase strategy with an initial nutrient‐sufficient reactor followed by a nutrient deprivation strategy could likely balance the desire for rapid and high biomass generation (124 mg/L) with a high oil content (50%) of Chlorella minutissima to maximize the total amount of oil produced for biodiesel production. Moreover, methyl palmitate (C16:0), methyl oleate (C18:1), methyl linoleate (C18:2), and methyl linolenate (C18:3) are the major components of Chlorella minutissima derived FAME, and choice of light source, intensity, and N starvation impacted the FAME composition of Chlorella minutissima. The optimized cultivation conditions resulted in higher growth rate, cell density, and oil content, making Chlorella minutissima a potentially suitable organism for biodiesel feedstock production. Biotechnol. Bioeng. 2011;108: 2280–2287. © 2011 Wiley Periodicals, Inc.     

Process Engineering for High-Cell-Density Cultivation of Lipid Rich Microalgal Biomass of <Emphasis Type="Italic">Chlorella</Emphasis> sp. FC2 IITG

In the present study, process engineering strategy was applied to achieve lipid-rich biomass with high density of Chlorella sp. FC2 IITG under photoautotrophic condition. The strategy involved medium optimization, intermittent feeding of limiting nutrients, dynamic change in light intensity, and decoupling growth and lipid induction phases. Medium optimization was performed using combinations of artificial neural network or response surface methodology with genetic algorithm (ANN-GA and RSM-GA). Further, a fed-batch operation was employed to achieve high cell density with intermittent feeding of nitrate and phosphate along with stepwise increase in light intensity. Finally, mutually exclusive biomass and lipid production phases were decoupled into two-stage cultivation process: biomass generation in first stage under nutrient sufficient condition followed by lipid enrichment through nitrogen starvation. The key findings were as follows: (i) ANN-GA resulted in an increase in biomass titer of 157 % (0.95 g L^?1) in shake flask and 42.8 % (1.0 g L^?1) in bioreactor against unoptimized medium at light intensity of 20 μE m^?2 s^?1; (ii) further optimization of light intensity in bioreactor gave significantly improved biomass titer of 5.6 g L^?1 at light intensity of 250 μE m^?2 s^?1; (iii) high cell density of 13.5 g L^?1 with biomass productivity of 675 mg L^?1 day^?1 was achieved with dynamic increase in light intensity and intermittent feeding of limiting nutrients; (iv) finally, two-phase cultivation resulted in biomass titer of 17.7 g L^?1 and total lipid productivity of 313 mg L^?1 day^?1 which was highest among Chlorella sp. under photoautotrophic condition.     

Neural network modeling of the light profile in a novel photobioreactor

An artificial neural network (ANN) was implemented to model the light profile pattern inside a photobioreactor (PBR) that uses a toroidal light arrangement. The PBR uses Tequila vinasses as culture medium and purple non-sulfur bacteria Rhodopseudomonas palustris as biocatalyzer. The performance of the ANN was tested for a number of conditions and compared to those obtained by using deterministic models. Both ANN and deterministic models were validated experimentally. In all cases, at low biomass concentration, model predictions yielded determination coefficients greater than 0.9. Nevertheless, ANN yielded the more accurate predictions of the light pattern, at both low and high biomass concentration, when the bioreactor radius, the depth, the rotational speed of the stirrer and the biomass concentration were incorporated in the ANN structure. In comparison, most of the deterministic models failed to correlate the empirical data at high biomass concentration. These results show the usefulness of ANNs in the modeling of the light profile pattern in photobioreactors.     

On-line biomass estimation in biosurfactant production process by <Emphasis Type="Italic">Candida lipolytica</Emphasis> UCP 988

Biomass is an important variable in biosurfactant production process. However, such bioprocess variable, usually, is collected by sampling and determined by off-line analysis, with significant time delay. Therefore, simple and reliable on-line biomass estimation procedures are highly desirable. An artificial neural network model (ANN) is presented for the on-line estimation of biomass concentration, in biosurfactant production by Candida lipolytica UCP 988, as a nonlinear function of pH and dissolved oxygen. Several configurations were evaluated while developing the optimal ANN model. The optimal ANN model consists of one hidden layer with four neurons. The performance of the ANN was checked using experimental data. The results obtained indicate a very good predictive capacity for the ANN-based software sensor with values of R2 of 0.969 and RMSE of 0.021 for biomass concentration. Estimated biomass using the ANN was proved to be a simple, robust and accurate method.     

Efficiency of neural network-based combinatorial model predicting optimal culture conditions for maximum biomass yields in hairy root cultures

Key message

ANN-based combinatorial model is proposed and its efficiency is assessed for the prediction of optimal culture conditions to achieve maximum productivity in a bioprocess in terms of high biomass.

Abstract

A neural network approach is utilized in combination with Hidden Markov concept to assess the optimal values of different environmental factors that result in maximum biomass productivity of cultured tissues after definite culture duration. Five hidden Markov models (HMMs) were derived for five test culture conditions, i.e. pH of liquid growth medium, volume of medium per culture vessel, sucrose concentration (%w/v) in growth medium, nitrate concentration (g/l) in the medium and finally the density of initial inoculum (g fresh weight) per culture vessel and their corresponding fresh weight biomass. The artificial neural network (ANN) model was represented as the function of these five Markov models, and the overall simulation of fresh weight biomass was done with this combinatorial ANN–HMM. The empirical results of Rauwolfia serpentina hairy roots were taken as model and compared with simulated results obtained from pure ANN and ANN–HMMs. The stochastic testing and Cronbach’s α-value of pure and combinatorial model revealed more internal consistency and skewed character (0.4635) in histogram of ANN–HMM compared to pure ANN (0.3804). The simulated results for optimal conditions of maximum fresh weight production obtained from ANN–HMM and ANN model closely resemble the experimentally optimized culture conditions based on which highest fresh weight was obtained. However, only 2.99 % deviation from the experimental values could be observed in the values obtained from combinatorial model when compared to the pure ANN model (5.44 %). This comparison showed 45 % better potential of combinatorial model for the prediction of optimal culture conditions for the best growth of hairy root cultures.     

Inulinase production in a batch bioreactor using agroindustrial residues as the substrate: experimental data and modeling

The production of inulinase employing agroindustrial residues as the substrate is a good alternative to reduce production costs and to minimize the environmental impact of disposing these residues in the environment. This study focused on the use of a phenomenological model and an artificial neural network (ANN) to simulate the inulinase production during the batch cultivation of the yeast Kluyveromyces marxianus NRRL Y-7571, employing a medium containing agroindustrial residues such as molasses, corn steep liquor and yeast extract. It was concluded that due to the complexity of the medium composition it was rather difficult to use a phenomenological model with sufficient accuracy. For this reason, an alternative and more cost-effective methodology based on ANN was adopted. The predictive capacity of the ANN was superior to that of the phenomenological model, indicating that the neural network approach could be used as an alternative in the predictive modeling of complex batch cultivations.     

Cultivation of the microalga <Emphasis Type="Italic">Neochloris oleoabundans</Emphasis> for biofuels production and other industrial applications (a review)

Microalgae cultivation for biofuels production and other applications has gained considerable interest recently. Despite their simple structures, microalgae can accumulate significant amounts of neutral lipids per dry cell weight compared to other energy crops. Neochloris oleoabundans is a promising microalga known for its high lipid content and biomass growth rate compared to other species cultivated for biofuels synthesis; therefore, it is considered as a suitable candidate for biodiesel synthesis. This review paper covers several key aspects associated with the cultivation and applications of the microalga N. oleoabundans. Biomass composition, factors affecting the growth, and biomass and lipid productivities of this species were addressed. In addition, different growth conditions as well as alternative readily available nutrient media to support the growth of N. oleoabundans were presented in this review.     

Progress in physicochemical parameters of microalgae cultivation for biofuel production

Microalgae have been exploited for biofuel generation in the current era due to its enormous energy content, fast cellular growth rate, inexpensive culture approaches, accumulation of inorganic compounds, and CO₂ sequestration. Currently, research is ongoing towards the advancement of the microalgae cultivation parameters to enhance the biomass yield. The main objective of this study was to delineate the progress of physicochemical parameters for microalgae cultivation such as gaseous transfer, mixing, light demand, temperature, pH, nutrients and the culture period. This review demonstrates the latest research trends on mass transfer coefficient of different microalgae culturing reactors, gas velocity optimization, light intensity, retention time, and radiance effects on microalgae cellular growth, temperature impact on chlorophyll production, and nutrient dosage ratios for cellulosic metabolism to avoid nutrient deprivation. Besides that, cultivation approaches for microalgae associated with mathematical modeling for different parameters, mechanisms of microalgal growth rate and doubling time have been elaborately described. Along with that, this review also documents potential lipid-carbohydrate-protein enriched microalgae candidates for biofuel, biomass productivity, and different cultivation conditions including open-pond cultivation, closed-loop cultivation, and photobioreactors. Various photobioreactor types, the microalgae strain, productivity, advantages, and limitations were tabulated. In line with microalgae cultivation, this study also outlines in detail numerous biofuels from microalgae.     

Bioprocess engineering of microalgae to optimize lipid production through nutrient management

Microalgae have been used commercially as a feedstock for the production of high-value compounds, pigments, cosmetics, and nutritional supplements. In addition, because of their rapid growth rates, high photosynthetic efficiency, and high lipid and protein content, commodity products including biodiesel, feed supplements, and polyunsaturated fatty acids derived from algal biomass are of current interest. Since microalgae lack non-photosynthetic structures and float in water, they do not need massive amounts of structural cellulose found in land plants. Thus, under optimal culture conditions, some oleaginous species can allocate up to 70 % of their biomass to lipids. Lipid production and its regulation in microalgae are species-specific and influenced by environmental conditions. Various strategies have been developed to improve lipid productivity and fatty acid composition to meet specific production goals. Manipulation of physiochemical parameters, trophic modes, and nutrient levels, known as process engineering, is a simple approach that leads to desired alterations in the biochemical composition of algal biomass, including lipid quantity and quality. In this paper, we review the effects of manipulating biochemical parameters such as necessary nutrients (C, N, P, S, Fe, and Si), NaCl concentration, and pH of culture medium to optimize lipid content and profile in some algae strains with commercial potential.     

An artificial neural network for biomass estimation from automatic pH control signal

This study developed an artificial neural network (ANN) to estimate the growth of microorganisms during a fermentation process. The ANN relies solely on the cumulative consumption of alkali and the buffer capacity, which were measured on-line from the on/off control signal and pH values through automatic pH control. The two input variables were monitored on-line from a series of different batch cultivations and used to train the ANN to estimate biomass. The ANN was refined by optimizing the network structure and by adopting various algorithms for its training. The software estimator successfully generated growth profiles that showed good agreement with the measured biomass of separate batch cultures carried out between at 25 and 35_C.     

Modeling of polygalacturonase enzyme activity and biomass production by <Emphasis Type="Italic">Aspergillus sojae</Emphasis> ATCC 20235

Aspergillus sojae, which is used in the making of koji, a characteristic Japanese food, is a potential candidate for the production of polygalacturonase (PG) enzyme, which of a major industrial significance. In this study, fermentation data of an A. sojae system were modeled by multiple linear regression (MLR) and artificial neural network (ANN) approaches to estimate PG activity and biomass. Nutrient concentrations, agitation speed, inoculum ratio and final pH of the fermentation medium were used as the inputs of the system. In addition to nutrient conditions, the final pH of the fermentation medium was also shown to be an effective parameter in the estimation of biomass concentration. The ANN parameters, such as number of hidden neurons, epochs and learning rate, were determined using a statistical approach. In the determination of network architecture, a cross-validation technique was used to test the ANN models. Goodness-of-fit of the regression and ANN models was measured by the R ² of cross-validated data and squared error of prediction. The PG activity and biomass were modeled with a 5-2-1 and 5-9-1 network topology, respectively. The models predicted enzyme activity with an R ² of 0.84 and biomass with an R ² value of 0.83, whereas the regression models predicted enzyme activity with an R ² of 0.84 and biomass with an R ² of 0.69.     

Strategies to enhance production of microalgal biomass and lipids for biofuel feedstock

ABSTRACT

Microalgae have enormous potential as feedstock for biofuel production compared with other sources, due to their high areal productivity, relatively low environmental impact, and low impact on food security. However, high production costs are the major limitation for commercialization of algal biofuels. Strategies to maximize biomass and lipid production are crucial for improving the economics of using microalgae for biofuels. Selection of suitable algal strains, preferably from indigenous habitats, and further improvement of those ‘platform strains’ using mutagenesis and genetic engineering approaches are desirable. Conventional approaches to improve biomass and lipid productivity of microalgae mainly involve manipulation of nutritional (e.g. nitrogen and phosphorus) and environmental (e.g. temperature, light and salinity) factors. Approaches such as the addition of phytohormones, genetic and metabolic engineering, and co-cultivation of microalgae with yeasts and bacteria are more recent strategies to enhance biomass and lipid productivity of microalgae. Improvement in culture systems and the use of a hybrid system (i.e. a combination of open ponds and photobioreactors) is another strategy to optimize algal biomass and lipid production. In addition, the use of low-cost substrates such as agri-industrial wastewater for the cultivation of microalgae will be a smart strategy to reduce production costs. Such systems not only generate high algal biomass and lipid productivity, but are also useful for bioremediation of wastewater and bioremoval of waste CO₂. The aim of this review is to highlight the advances in the use of various strategies to enhance production of algal biomass and lipids for biofuel feedstock.     

Mixed culture of oleaginous yeast Rhodotorula glutinis and microalga Chlorella vulgaris for lipid production from industrial wastes and its use as biodiesel feedstock

A mixed culture of oleaginous yeast Rhodotorula glutinis and microalga Chlorella vulgaris was performed to enhance lipid production from industrial wastes. These included effluent from seafood processing plant and molasses from sugar cane plant. In the mixed culture, the yeast grew faster and the lipid production was higher than that in the pure cultures. This could be because microalga acted as an oxygen generator for yeast, while yeast provided CO(2) to microalga and both carried out the production of lipids. The optimal conditions for lipid production by the mixed culture were as follows: ratio of yeast to microalga at 1:1; initial pH at 5.0; molasses concentration at 1%; shaking speed at 200 rpm; and light intensity at 5.0 klux under 16:8 hours light and dark cycles. Under these conditions, the highest biomass of 4.63±0.15 g/L and lipid production of 2.88±0.16 g/L were obtained after five days of cultivation. In addition, the plant oil-like fatty acid composition of yeast and microalgal lipids suggested their high potential for use as biodiesel feedstock.     

Microalgal carbohydrates: an overview of the factors influencing carbohydrates production, and of main bioconversion technologies for production of biofuels

Microalgal biomass seems to be a promising feedstock for biofuel generation. Microalgae have relative high photosynthetic efficiencies, high growth rates, and some species can thrive in brackish water or seawater and wastewater from the food- and agro-industrial sector. Today, the main interest in research is the cultivation of microalgae for lipids production to generate biodiesel. However, there are several other biological or thermochemical conversion technologies, in which microalgal biomass could be used as substrate. However, the high protein content or the low carbohydrate content of the majority of the microalgal species might be a constraint for their possible use in these technologies. Moreover, in the majority of biomass conversion technologies, carbohydrates are the main substrate for production of biofuels. Nevertheless, microalgae biomass composition could be manipulated by several cultivation techniques, such as nutrient starvation or other stressed environmental conditions, which cause the microalgae to accumulate carbohydrates. This paper attempts to give a general overview of techniques that can be used for increasing the microalgal biomass carbohydrate content. In addition, biomass conversion technologies, related to the conversion of carbohydrates into biofuels are discussed.     

Optimization of culture conditions for the production of Pleuromutilin from Pleurotus Mutilus using a hybrid method based on central composite design, neural network, and particle swarm optimization

This study aims at optimizing the culture conditions (agitation speed, temperature and pH) of the Pleuromutilin production by Pleurotus mutilus. A hybrid methodology including a central composite design (CCD), an artificial neural network (ANN), and a particle swarm optimization algorithm (PSO) was used. Specifically, the CCD and ANN were used for conducting experiments and modeling the non-linear process, respectively. The PSO was used for two purposes: Replacing the standard back propagation in training the ANN (PSONN) and optimizing the process. In comparison to the response surface methodology (RSM) and to the Bayesian regularization neural network (BRNN), PSONN model has shown the highest modeling ability. Under this hybrid approach (PSONN-PSO), the optimum levels of culture conditions were: 242 rpm agitation speed; temperature 26.88 and pH 6.06. A production of 10,074 ± 500 ??g/g, which was in very good agreement with the prediction (10,149 ??g/g), was observed in verification experiment. The hybrid PSONN-PSO gave a yield of 27.5% greater than that obtained by the hybrid BRNN-PSO. This work shows that the combination of PSONN with the generic PSO algorithm has a good predictability and a good accuracy for bio-process optimization. This hybrid approach is sufficiently general and thus can be helpful for modeling and optimization of other industrial bio-processes.     

微藻废水生物处理技术研究进展

微藻因生长速率快、细胞脂质含量高及具有生物隔离二氧化碳能力,已作为新一代生物质能源受到广泛关注.然而,投入大量淡水资源并需在生长期间持续提供营养物质已成为规模化培育微藻的主要障碍.将微藻培育系统与废水处理相结合是经济可行的污水资源化方案.基于微藻生长期间对氮磷等营养物质的利用机制,本文综述了微藻在各类废水生物处理过程中的应用情况,着重分析了其对废水中有机与无机化合物、重金属以及病原体的去除或抑制能力.同时,考察了废水初始营养物浓度、光照、温度、pH与盐度以及气体交换量等环境因素对微藻生长代谢的影响.此外,结合微藻规模化应用所面临的问题,对微藻废水处理技术的应用前景及发展方向进行了展望,旨在为水生态系统的建设与管理提供参考.     

A robust soft sensor to monitor 1,3-propanediol fermentation process by Clostridium butyricum based on artificial neural network

With the aggravation of environmental pollution and energy crisis, the sustainable microbial fermentation process of converting glycerol to 1,3-propanediol (1,3-PDO) has become an attractive alternative. However, the difficulty in the online measurement of glycerol and 1,3-PDO creates a barrier to the fermentation process and then leads to the residual glycerol and therefore, its wastage. Thus, in the present study, the four-input artificial neural network (ANN) model was developed successfully to predict the concentration of glycerol, 1,3-PDO, and biomass with high accuracy. Moreover, an ANN model combined with a kinetic model was also successfully developed to simulate the fed-batch fermentation process accurately. Hence, a soft sensor from the ANN model based on NaOH-related parameters has been successfully developed which cannot only be applied in software to solve the difficulty of glycerol and 1,3-PDO online measurement during the industrialization process, but also offer insight and reference for similar fermentation processes.     

不同生境中柔毛淫羊藿形态特征及其有效成分差异分析

采用高效液相色谱法和紫外分光光度法分别测定了不同生境中柔毛淫羊藿的有效成分,并运用one-way ANOVA统计分析了不同生境中柔毛淫羊藿有效成分的差异以及形态参数和生物量的差异。结果表明:3个生境中,相对光照强度较低的生境,其形态参数和植株各个部位的生物量均要低于光照强度相对较强的生境,但3个生境中的整体叶形基本一致;三个生境中淫羊藿总黄酮和淫羊藿苷含量在光照强度较高的生境中要大于光照较低的生境;柔毛淫羊藿植株各个部位的有效成分大小为叶>根>茎;在不同生境中柔毛淫羊藿叶片的黄酮和淫羊藿苷含量都能达到用药标准,其余的部位则不能达到用药标准。因此在人工栽培柔毛淫羊藿时,应模拟野外生长条件,以期提高柔毛淫羊藿质量,替代和保护野生资源。     

Artificial neural networks (ANN) approach for modeling of removal of Lanaset Red G on Chara contraria

A three-layer artificial neural network (ANN) was constructed to predict the removal efficiency of Lanaset Red (LR) G on Chara contraria based on 2304 experimental sets. The effects of operating variables (particle size, adsorbent dosage, pH regimes, dye concentration, and contact time) were studied to optimize the sorption conditions of this dye. The operating variables were used as the input to the constructed neural network to predict the dye uptake at any time as the output. This adsorbent was characterized by FTIR. Pseudo second-order model was also fitted to the experimental data. According to values of error analyses and determinations coefficient, the ANN was more appropriate to describe this adsorption process. Result of this model indicated that pH regimes had the highest importance effect (49%) on the dye uptake.