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Author: search.filters.author.Kumari, Sheena K.Has files: Yes

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    Artificial intelligence for the evaluation of operational parameters influencing Nitrification and Nitrifiers in an activated sludge process
    (Springer Science+Business Media, 2016) Awolusi, Oluyemi Olatunji; Nasr, Mahmoud; Kumari, Sheena K.; Bux, Faizal
    Abstract Nitrification at a full-scale activated sludge plant treating municipal wastewater was monitored over a period of 237 days. A combination of fluorescent in situ hybridiza-tion (FISH) and quantitative real-time polymerase chain reac-tion (qPCR) were used for identifying and quantifying the dominant nitrifiers in the plant. Adaptive neuro-fuzzy infer-ence system (ANFIS), Pearson’s correlation coefficient, and quadratic models were employed in evaluating the plant oper-ational conditions that influence the nitrification performance. The ammonia-oxidizing bacteria (AOB) abundance was with-in the range of 1.55 × 108–1.65 × 1010 copies L−1, while Nitrobacter spp. and Nitrospira spp. were 9.32 × 109–1.40 × 1011 copies L− 1 and 2.39 × 109 –3.76 × 1010 copies L−1, respectively. Specific nitrification rate (qN)was significantly affected by temperature (r 0.726, p 0.002), hy-draulic retention time (HRT) (r −0.651, p 0.009), and ammo-nia loading rate (ALR) (r 0.571, p 0.026). Additionally, AOB was considerably influenced by HRT (r −0.741, p 0.002) and temperature (r 0.517, p 0.048), while HRT negatively impact-ed Nitrospira spp. (r −0.627, p 0.012). A quadratic combina-tion of HRT and food-to-microorganism (F/M) ratio also im-pacted qN (r2 0.50), AOB (r2 0.61), and Nitrospira spp. (r2 0.72), while Nitrobacter spp. was considerably influenced by a polynomial function of F/M ratio and temperature (r2 0.49). The study demonstrated that ANFIS could be used as a tool to describe the factors influencing nitrification process at full-scale wastewater treatment plants.
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    Trends and novel strategies for enhancing lipid accumulation and quality in microalgae
    (Elsevier, 2016) Singh, Poonam; Kumari, Sheena K.; Guldhe, Abhishek; Rawat, Ismail; Misra, Rohit; Bux, Faizal
    In order to realize the potential of microalgal biodiesel there is a need for substantial impetus involving interventions to radically improve lipid yields upstream. Nutrient stress and alteration to cultivation conditions are commonly used lipid enhancement strategies in microalgae. The main bottleneck of applying conventional strategies is their scalability as some of these strategies incur additional cost and energy. Novel lipid enhancement strategies have emerged to research forefront to overcome these challenges. In this review, the latest trends in microalgal lipid enhancement strategies, possible solutions and future directions are critically discussed. Advanced strategies such as combined nutrient and culti-vation condition stress, microalgae–bacteria interactions, use of phytohormones EDTA and chemical additives, improving light conditions using LED, dyes and paints, and gene expression analysis are described. Molecular approaches such as metabolic and genetic engineering are emerging as the potential lipid enhancing strategies. Recent advancements in gene expression studies, genetic and metabolic engineering have shown promising results in enhancing lipid productivity in microalgae; however environmental risk and long term viability are still major challenges.