This work was focused on understanding the pyrolysis of Typha latifolia. Kinetics, thermodynamics parameters and pyrolysis reaction mechanism were studied using thermogravimetric data. Based on activation energies and conversion points, two regions of pyrolysis were established. Region-I occurred between the conversion rate 0.1-0.4 with peak temperatures 538K, 555K, 556K at the heating rates of 10Kmin-1, 30Kmin-1, and 50Kmin-1, respectively. Similarly, the Region-II occurred between 0.4 and 0.8 with peak temperatures of 606K, 621K, 623K at same heating rates. The best model was diffusion mechanism in Region-I. In Region-II, the reaction order was shown to be 2nd and 3rd. The values of activation energy calculated using FWO and KAS methods (134-204kJmol-1) remained same in both regions reflecting that the best reaction mechanism was predicted. Kinetics and thermodynamic parameters including E, ΔH, ΔS, ΔG shown that T. latifolia biomass is a remarkable feedstock for bioenergy.

The ultimate goal of metabolic engineering is to produce desired compounds on an industrial scale in a cost effective manner. To address challenges in metabolic engineering, computational strain optimization algorithms based on genome-scale metabolic models have increasingly been used to aid in overproducing products of interest. However, most of these strain optimization algorithms utilize a metabolic network alone, with few approaches providing strategies that also include transcriptional regulation. Moreover previous integrated approaches generally require a pre-existing regulatory network. In this study, we developed a novel strain design algorithm, named OptRAM (Optimization of Regulatory And Metabolic Networks), which can identify combinatorial optimization strategies including overexpression, knockdown or knockout of both metabolic genes and transcription factors. OptRAM is based on our previous IDREAM integrated network framework, which makes it able to deduce a regulatory network from data. OptRAM uses simulated annealing with a novel objective function, which can ensure a favorable coupling between desired chemical and cell growth. The other advance we propose is a systematic evaluation metric of multiple solutions, by considering the essential genes, flux variation, and engineering manipulation cost. We applied OptRAM to generate strain designs for succinate, 2,3-butanediol, and ethanol overproduction in yeast, which predicted high minimum predicted target production rate compared with other methods and previous literature values. Moreover, most of the genes and TFs proposed to be altered by OptRAM in these scenarios have been validated by modification of the exact genes or the target genes regulated by the TFs, for overproduction of these desired compounds by in vivo experiments cataloged in the LASER database. Particularly, we successfully validated the predicted strain optimization strategy for ethanol production by fermentation experiment. In conclusion, OptRAM can provide a useful approach that leverages an integrated transcriptional regulatory network and metabolic network to guide metabolic engineering applications. Computational strain design algorithms based on genome-scale metabolic models have increasingly been used to guide rational strain design for metabolic engineering. However, most strain optimization algorithms only utilize a metabolic network alone and cannot provide strategies that also involve transcriptional regulation. In this paper, we developed a novel strain design algorithm, named OptRAM (Optimization of Regulatory And Metabolic Network), which can identify combinatorial optimization strategies including overexpression, knockdown or knockout of both transcription factors and metabolic genes, based on our previous IDREAM integrated network framework. OptRAM uses simulated annealing with a novel objective function, which can ensure a favorable coupling between the production of a desired chemical and cell growth. This strategy can be deployed for strain design of bacteria, archaea or eukaryotes. The other advantage of OptRAM compared with previous heuristic approaches is that we systematically evaluated the implementation cost of different solutions and selected strain designs which are more likely to be achievable in experiments. Through the in-silico strain design case studies for producing succinate, 2,3-butanediol, and ethanol in yeast, we demonstrated that OptRAM can identify strategies that increase production beyond what is seen currently, or found as potential designs using alternative methods. We also validated the modified genes chosen by OptRAM in example cases against previous in vivo experiments in the LASER database. Additionally, we experimentally validated the ethanol strain design by evaluating its performance in fermentation. OptRAM provides a robust approach to strain design across gene regulatory network modification and metabolic engineering.

Hydrodeoxygenation (HDO) of bio-oil derived from liquefaction of a palm empty fruit bunch (EFB) in glycerol was investigated. To enhance the heating value and reduce the oxygen content of upgraded bio-oil, hydrodeoxygenation of light bio-oil over Ni- and Co-based catalysts on an Al₂O₃ support was performed in a rotating-bed reactor. Two consecutive steps were conducted to produce bio-oil from EFB including (1) microwave-assisted wet torrefaction of EFB and (2) solvothermolysis liquefaction of treated EFB in a Na₂CO₃/glycerol system. The HDO of as-prepared bio-oil was subsequently performed in a unique design reactor possessing a rotating catalyst bed for efficient interaction of a catalyst with bio-oil and facile separation of the catalyst from upgraded bio-oil after the reaction. The reaction was carried out in the presence of each mono- or bimetallic catalyst, namely, Co/Al₂O₃, Ni/Al₂O₃, NiMo/Al₂O₃, and CoMo/Al₂O₃, packed in the rotating-mesh host with a rotation speed of 250 rpm and kept at 300 and 350 °C, 2 MPa hydrogen for 1 h. From the results, the qualities of upgraded bio-oil were substantially improved for all catalysts tested in terms of oxygen reduction and increased high heating value (HHV). Particularly, the NiMo/Al₂O₃ catalyst exhibited the most promising catalyst, providing favorable bio-oil yield and HHV. Remarkably greater energy ratios and carbon recovery together with high H/O, C/O, and H/C ratios were additionally achieved from the NiMo/Al₂O₃ catalyst compared with other catalysts. Cyclopentanone and cyclopentene were the main olefins found in hydrodeoxygenated bio-oil derived from liquefied EFB. It was observed that cyclopentene was first generated and subsequently converted to cyclopentanone under the hydrogenation reaction. These compounds can be further used as a building block in the synthesis of jet-fuel range cycloalkanes.