Application of microalga Chlamydomonas sp. for biosorptive removal of a textile dye from contaminated water: modelling by a neural network

Microalgae with artificial intelligence: A digitalized perspective on genetics, systems and products

With recent advances in novel gene-editing tools such as RNAi, ZFNs, TALENs, and CRISPR-Cas9, the possibility of altering microalgae toward designed properties for various application is becoming a reality. Alteration of microalgae genomes can modify metabolic pathways to give elevated yields in lipids, biomass, and other components. The potential of such genetically optimized microalgae can give a "domino effect" in further providing optimization leverages down the supply chain, in aspects such as cultivation, processing, system design, process integration, and revolutionary products. However, the current level of understanding the functional information of various microalgae gene sequences is still primitive and insufficient as microalgae genome sequences are long and complex. From this perspective, this work proposes to link up this knowledge gap between microalgae genetic information and optimized bioproducts using Artificial Intelligence (AI). With the recent acceleration of AI research, large and complex data from microalgae research can be properly analyzed by combining the cutting-edge of both fields. In this work, the most suitable class of AI algorithms (such as active learning, semi-supervised learning, and meta-learning) are discussed for different cases of microalgae applications. This work concisely reviews the current state of the research milestones and highlight some of the state-of-art that has been carried out, providing insightful future pathways. The utilization of AI algorithms in microalgae cultivation, system optimization, and other aspects of the supply chain is also discussed. This work opens the pathway to a digitalized future for microalgae research and applications.

A low-cost wheat bran medium for biodegradation of the benzidine-based carcinogenic dye Trypan Blue using a microbial consortium

Environmental release of benzidine-based dyes is a matter of health concern. Here, a microbial consortium was enriched from textile dye contaminated soils and investigated for biodegradation of the carcinogenic benzidine-based dye Trypan Blue using wheat bran (WB) as growth medium. The PCR-DGGE analysis of enriched microbial consortium revealed the presence of 15 different bacteria. Decolorization studies suggested that the microbial consortium has high metabolic activity towards Trypan Blue as complete removal of 50 mg∙L⁻¹ dye was observed within 24 h at 30 ± 0.2 °C and pH 7. Significant reduction in TOC (64%) and COD (88%) of dye decolorized broths confirmed mineralization. Induction in azoreductase (500%), NADH-DCIP reductase (264%) and laccase (275%) proved enzymatic decolorization of dye. HPLC analysis of dye decolorized products showed the formation of six metabolites while the FTIR spectrum indicated removal of diazo bonds at 1612.30 and 1581.34 cm⁻¹. The proposed dye degradation pathway based on GC-MS and enzyme analysis suggested the formation of two low molecular weight intermediates. Phytotoxicity and acute toxicity studies revealed the less toxic nature of the dye degradation products. These results provide experimental evidence for the utilization of agricultural waste as a novel low-cost growth medium for biodegradation of benzidine-based dyes, and suggested the potential of the microbial consortium in detoxification.

The effects of salinity on the growth and biochemical properties of Chlamydomonas mexicana GU732420 cultivated in municipal wastewater

A freshwater microalga Chlamydomonas mexicana was grown on municipal wastewater with different levels of salinity up to 400 mmol/L NaCl, and the biochemical properties were characterized after 10 days of cultivation. C. mexicana showed the higher specific growth rates for 100 and 200mmol/L NaCl. Nitrogen was completely removed within 10 days as a result of algal growth promoted by the addition of 200-400 mmol/L NaCl. Phosphorus removal increased from 77-84% as the concentration of NaCI increased from 100 to 400 mmol/L. The highest removal of total inorganic carbon (66%) was obtained with the addition of 200 mmol/L NaCl. The lipid content increased from 17% to 38% as the concentration of NaCl increased from 0 to 400mmol/L. The total fatty acid content and glycerol yield of C. mexicana increased 1.8- and 4-fold in wastewater amended with NaCl, respectively. Fatty acids accumulated in the algal biomass were mainly composed of palmitic (27-29%), y-linolenic (27-30%), and linolelaidic acids (16-18%). The optimal condition for fatty acids production in C. mexicana was observed when the municipal wastewater was amended with 100-200 mmol/L NaCl with a simultaneous removal of nutrients.

The transformation mechanism of nitrobenzene in the present of a species of cyanobacteria Microcystis aeruginosa

The transformation mechanism of nitrobenzene (NB) with Microcystis aeruginosa was investigated by a series of laboratory-scale experiments. The result showed only a small fraction of NB can be adsorbed by M. aeruginosa. The adsorption was responsible to the transformation of NB in M. aeruginosa solution but was not the primary cause. The variation of cell activity and illumination could affect the transformation of NB with M. aeruginosa, which indicated that M. aeruginosa have the ability to biodegrade NB. Metabolic intermediate products analysis indicated that M. aeruginosa can reduce NB to aniline (AN), and NB reductase, induced by NB, was the key enzyme during the reduction process. M. aeruginosa cannot further degrade AN and may prevent the volatilization of AN, causing the accumulation of AN in the solution for up to 3 days. Only a small proportion of AN (less than 5%) can be degraded to acetaldehyde and acetone by photolysis in 48 h. The total concentration of nitrogen aromatic compounds is invariant at first, and then decreases after 72 h incubation via a complex process including adsorption, biodegradation, volatilization and photolysis processes.

Modelling of multi-nutrient interactions in growth of the dinoflagellate microalga Protoceratium reticulatum using artificial neural networks

This study examines the use of artificial neural networks as predictive tools for the growth of the dinoflagellate microalga Protoceratium reticulatum. Feed-forward back-propagation neural networks (FBN), using Levenberg-Marquardt back-propagation or Bayesian regularization as training functions, offered the best results in terms of representing the nonlinear interactions among all nutrients in a culture medium containing 26 different components. A FBN configuration of 26-14-1 layers was selected. The FBN model was trained using more than 500 culture experiments on a shake flask scale. Garson's algorithm provided a valuable means of evaluating the relative importance of nutrients in terms of microalgal growth. Microelements and vitamins had a significant importance (approximately 70%) in relation to macronutrients (nearly 25%), despite their concentrations in the culture medium being various orders of magnitude smaller. The approach presented here may be useful for modelling multi-nutrient interactions in photobioreactors.

Degradation of dyes from aqueous solution by Fenton processes: a review

Several industries are using dyes as coloring agents. The effluents from these industries are increasingly becoming an environmental problem. The removal of dyes from aqueous solution has a great potential in the field of environmental engineering. This paper reviews the classification, characteristics, and problems of dyes in detail. Advantages and disadvantages of different methods used for dye removal are also analyzed. Among these methods, Fenton process-based advanced oxidation processes are an emerging prospect in the field of dye removal. Fenton processes have been classified and represented as "Fenton circle". This paper analyzes the recent studies on Fenton processes. The studies include analyzing different configurations of reactors used for dye removal, its efficiency, and the effects of various operating parameters such as pH, catalyst concentration, H2O2 concentration, initial dye concentration, and temperature of Fenton processes. From the present study, it can be conclude that Fenton processes are very effective and environmentally friendly methods for dye removal.

Chemometrics approach for determination and optimization of simultaneous photooxidative decolourization of a mixture of three textile dyes

In this paper simultaneous photooxidation of three textile dyes (CI Basic Red 46 (BR46), Malachite Green (MG) and CI Basic Blue 3 (BB3)) by UV/H2O2 process is reported. A rapid analytical methodology based on recording UV-Vis spectra during the photooxidative decolourization process and the data treatment using multivariate curve resolution with alternating least squares (MCR-ALS) was developed. The three textile dyes were quantified simultaneously despite the overlap of their spectra. Also, the central composite design was applied for optimization of the photooxidative decolourization of the solution containing three dyes using the UV/H2O2 process. The investigated factors (variables) were the reaction time, initial concentration of the three dyes and initial concentration of H2O2. The optimum initial concentration of the three dyes, H2O2 initial concentration and reaction time were found to be 4 mg/L, 48 mg/L and 30 min, respectively. The photodegradation products of three dyes were identified by the GC-MS technique.

Bio-reaction of nitrobenzene with Microcystis aeruginosa: characteristics, kinetics and application

The bio-reaction of nitrobenzene (NB) with Microcystis aeruginosa was investigated at different initial algal densities and NB concentrations by performing static experiments. The results showed that the elimination of NB was enhanced by the bio-reaction, and the reaction rate varied as a function of the reaction time. Moreover, the reaction rate was significantly affected by the algal density and NB concentration. A kinetic analysis showed that the elimination of NB in a solution without algae appeared to be pseudo-first-order with respect to the NB concentration, whereas a first-order model was too oversimplified to describe the elimination of NB in a solution with algae. Assuming that different algal cells have the same effect on the bio-reaction under the same conditions, the bio-reaction can be described as dC(NB) = -k(0)C(A)(m)A(NB)(n)dt (where k(0) is the reaction rate constant, C(A) is the algae density and C(NB) is the concentration of NB). When the growth of algae was not considered, the value of k(0), m and n were 8.170 × 10(-4), 0.5887 and 1.692, respectively. Alternatively, when algae were in the exponential growth phase, the value of k(0), m and n were 1.6871 × 10(-5), 0.7248 and 2.5407, respectively, according to a nonlinear fitting analysis. The kinetic model was also used to elucidate the effect of nutritional limitation on the bio-reaction.

Optimization of photocatalytic degradation of sulphonated diazo dye C.I. Reactive Green 19 using ceramic-coated TiO2 nanoparticles

Optimization of photocatalytic degradation of C.I. Reactive Green 19 (RG 19) under UV light irradiation using ceramic-coated TiO2 nanoparticles in a continuous circulation rectangular photoreactor was studied. The used catalyst was TiO2 Millennium PC-500 (crystallite mean size 8 nm) immobilized on ceramic plates. A central composite design was used for optimization of the UV/TiO2 process. Predicted values of decolorization efficiency were found to be in good agreement with experimental values (R2 = 0.97 and Adj-R2 = 0.91). Optimization results showed that maximum decolorization efficiency was achieved at the optimum conditions of: initial dye concentration 10 mg/L, UV light intensity 47.2 W/m2, flow rate 150 mL/min and reaction time 240 min. Photocatalytic mineralization of RG 19 was monitored by chemical oxygen demand (COD) decrease and changes in the UV-Vis spectrum.

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.