Feroz Mahomed Swalaha

OVERVIEW OF THE POTENTIAL OF MICROALGAE FOR CO2 SEQUESTRATION

An economic and environmentally friendly approach of overcoming the problem of fossil CO2 emissions would be to reuse it through fixation into biomass. Carbon dioxide (CO2), which is the basis for the formation of complex sugars by green plants and microalgae through photosynthesis, has been shown to significantly increase the growth rates of certain microalgal species. Microalgae possess a greater capacity to fix CO2 compared to C4 plants. Selection of appropriate microalgal strains is based on the CO2 fixation and tolerance capability together with lipid potential, both of which are a function of biomass productivity. Microalgae can be propagated in open raceway ponds or closed photobioreactors. Biological CO2 fixation also depends on the tolerance of selected strains to high temperatures and the amount of CO2 present in flue gas, together with SOx and NOx. Potential uses of microalgal biomass after sequestration could include biodiesel production, fodder for livestock, production of colorants and vitamins. This review summarizes commonly employed microalgal species as well as the physiological pathway involved in the biochemistry of CO2 fixation. It also presents an outlook on microalgal propagation systems for CO2 sequestration as well as a summary on the life cycle analysis of the process.

Constructed wetlands : a future alternative wastewater treatment technology

Wastewater treatment will always pose problems if there are no new alternative technologies in place to replace the currently available technologies. More recently, it has been estimated that developing countries will run out of water by 2050. This is a course for concern not only to the communities but also a challenge to the scientist to find new ways of wastewater recycling. Water losses can be avoided through implementation of easy and inexpensive technologies for wastewater treatment. Environmental concerns over insufficiently performing septic systems and high expenses in the construction of sewer systems as well as their operations with centralized water purification systems have spurred investigation into the appropriateness of the use of wetland technology for wastewater treatment. Constructed wetland efficiency and potential application in wastewater treatment has been reported decades ago. However, the logistics and research for their commercial applications in wastewater treatment has not been documented in details. Research has shown that wetland systems can achieve high treatment efficiencies with regards to both organic and inorganic nutrients as well as pathogen removal if properly managed and efficiently utilized. This can have a profound effect in the management and conservation of our scarce and yet depleting water resources. Keywords : Constructed wetlands, rhizofiltration, microbial biofilms, wastewater treatment, treatment mechanism African Journal of Biotechnology Vol. 12(29), pp. 4542-4553

Physiological responses of carbon-sequestering microalgae to elevated carbon regimes

In order to identify a high carbon-sequestering microalgal strain, the physiological effect of different concentrations of carbon sources on microalgae growth was investigated. Five indigenous strains (I-1, I-2, I-3, I-4 and I-5) and a reference strain (I-0: Coccolithus pelagicus 913/3) were subjected to CO2 concentrations of 0.03–15% and NaHCO3 of 0.05–2 g CO2 l–1. The logistic model was applied for data fitting, as well as for estimation of the maximum growth rate (μmax) and the biomass carrying capacity (Bmax). Amongst the five indigenous strains, I-3 was similar to the reference strain with regards to biomass production values. The Bmax of I-3 significantly increased from 214 to 828 mg l–1 when CO2 concentration was increased from 0.03 to 15% (r = 0.955, P = 0.012). Additionally, the Bmax of I-3 increased with increasing NaHCO3 (r = 0.885, P = 0.046) and was recorded at 153 mg l–1 (at 0.05 g CO2 l–1) and 774 mg l–1 at (2 g CO2 l–1). Relative electron transport rate (rETR) and maximum quantum yield (Fv/Fm) were also applied to assess the impact of elevated carbon sources on the microalgal cells at the physiological level. Isolate I-3 displayed the highest rETR confirming its tolerance to higher quantities of carbon. Additionally, the decline in Fv/Fm with increasing carbon was similar for strains I-3 and the reference strain. Based on partial 28s ribosomal RNA gene sequencing, strain I-3 was homologous to the ribosomal genes of Chlorella sp.

A logistic model for the remediation of filamentous bulking in a biological nutrient removal wastewater treatment plant

Biological nutrient removal (BNR) systems across the globe frequently experience bulking and foaming episodes, which present operational challenges such as poor sludge settling due to excessive filamentous bacteria. A full-scale BNR plant treating primarily domestic wastewater was monitored over a period of 1 year to investigate filamentous bacterial growth response under various plant operating parameters. Identification of filamentous bacteria by conventional microscopy and fluorescent in situ hybridisation indicated the dominance of Eikelboom Type021N, Thiothrix spp., Eikelboom Type 1851 and Eikelboom Type 0092. A cumulative logit model (CLM) was applied to elucidate significant relationships between the filamentous bacteria and plant operational parameters. The model could predict the potential abundance of dominant filamentous bacteria in relation to wastewater treatment plant operational parameters. Data obtained from the model corroborated with previous findings on the dominance of most filaments identified, except for Type 0092, which exhibited some unique traits. With further validation, the model could be successfully applied for identifying specific parameters which could contribute towards filamentous bulking, thus, providing a useful tool for regulating specific filamentous growth in full-scale wastewater treatment plants.

Optimization of biogas generation using anaerobic digestion models and computational intelligence approaches

Abstract Anaerobic digestion (AD) technology has become popular and is widely used due to its ability to produce renewable energy from wastes. The bioenergy produced in anaerobic digesters could be directly used as fuel, thereby reducing the release of biogas to the atmosphere. Due to the limited knowledge on the different process disturbances and microbial composition that are vital for the efficient operation of AD systems, models and control strategies with respect to external influences are needed without wasting time and resources. Different simple and complex mechanistic and data-driven modeling approaches have been developed to describe the processes taking place in the AD system. Microbial activities have been incorporated in some of these models to serve as a predictive tool in biological processes. The flexibility and power of computational intelligence of evolutionary algorithms (EAs) as direct search algorithms to solve multiobjective problems and generate Pareto-optimal solutions have also been exploited. Thus, this paper reviews state-of-the-art models based on the computational optimization methods for renewable and sustainable energy optimization. This paper discusses the different types of model approaches to enhance AD processes for bioenergy generation. The optimization and control strategies using EAs for advanced reactor performance and biogas production are highlighted. This information would be of interest to a dynamic group of researchers, including microbiologists and process engineers, thereby offering the latest research advances and importance of AD technology in the production of renewable energy.

Multi-objective Optimization of Methane Producing UASB Reactor Using a Combined Pareto Multi–objective Differential Evolution Algorithm (CPMDE)

Multi–objective optimization of an operating industrial wastewater treatment plant was carried out using combined Pareto multi–objective differential evolution (CPMDE) algorithm. The algorithm combines methods of Pareto ranking and Pareto dominance selections to implement a novel selection scheme at each generation. Modified methane generation and the Stover–Kincannon kinetic mathematical models were formulated for optimization. The conflicting objective functions that are optimized in this study include, maximization of volumetric methane production rate in the biogas produced at a lower hydraulic retention time and optimum temperature; minimization of effluent substrate concentration in order to meet the environmental discharge requirements based on the standard discharge limit, and finally, the minimization of biomass washout from the reactor. Wastewater flow rate, hydraulic retention time, efficiency of substrate utilization within the reactor, influent substrate concentration and operational temperature are the important decision variables related to this process. A set of non-dominated solutions with the high methane production rate at lower biomass and almost constant solution for the effluent concentration was obtained for the multi-objective optimization problem. In this study, the simulation results showed that the CPMDE approach can generate a better Pareto-front of the selected problem and its ability to solve unconstrained, constrained and real-world optimization problem was also demonstrated.

Fuzzy intelligence for investigating the correlation between growth performance and metabolic yields of a Chlorella sp exposed to various flue gas schemes

A Chlorella sp. was cultivated in a photobioreactor under different experimental conditions to investigate its acclimation to high-CO2 exposures. When the microalgae was grown under controlled flue gas sparging and optimised nutrients, the biomass concentration increased to 3.415±0.145gL-1 and the maximum protein yield was obtained (57.500±0.351% ww-1). However, when the culture was exposed to continuous flue gas, the lowest biomass growth (1.665±0.129gL-1) was noted. Under these conditions, high carbohydrate and lipid values were recorded (38.600±1.320% ww-1 and 30.200±0.150% ww-1), respectively. A Sugeno-type fuzzy model was employed to understand the correlation between peak biomass concentration (Bmax), CO2 uptake rate (qCO2), and maximum relative electron transport rate (rETRmax) as inputs and carbohydrate, protein, and lipid yields as outputs. Results of the model were in agreement with the experimental data (r2-value >0.985).