Modular Supply Network Optimization of Renewable Ammonia and Methanol Co-production

To reduce the use of fossil fuels and other carbonaceous fuels, renewable energy sources such as solar, wind, geothermal energy have been suggested to be promising alternative energy that guarantee sustainable and clean environment. However, the availability of renewable energy has been limited due to its dependence on weather and geographical location. This challenge is intended to be solved by the utilization of the renewable energy in the production of chemical energy carriers. Hydrogen has been proposed as a potential renewable energy carrier, however, its chemical instability and high liquefaction energy makes researchers seek for other alternative energy carriers. Ammonia and methanol can serve as promising alternative energy carriers due to their chemical stability at room temperature, low liquefaction energy, high energy value. The co-production of these high energy dense energy carriers offers economic and environmental advantages since their synthesis involve the direct utilization of CO2 and common unit operations. This problem report aims to review the optimization of the co-production of methanol and ammonia from renewable energy. Form this review, research challenges and opportunities are identified in the following areas: (i) optimization of methanol and ammonia co-production under renewable and demand uncertainty, (ii) impacts of the modular exponent on the feasibility of co-production of ammonia and methanol, and (iii) development of modern computational tools for systems-based analysis

Overview of Multiobjective Optimization Methods in in Silico Metabolic Engineering

Multiobjective optimization requires of finding a trade-off between multiple objectives. However, most of the objectives are contradict towards each other, thus makes it difficult for the traditional approaches to find a solution that satisfies all objectives. Fortunately, the problems are able to solve by the aid of Pareto methods. Meanwhile, in in silico Metabolic Engineering, the identification of reaction knockout strategies that produce mutant strains with a permissible growth rate and product rate of desired metabolites is still hindered. Previously, Evolutionary Algorithms (EAs) has been successfully used in determining the reaction knockout strategies. Nevertheless, most methods work by optimizing one objective function, which is growth rate or production rate. Furthermore, in bioprocesses, it involves multiple and conflicting objectives. In this review, we aim to show the different multiobjective evolutionary optimization methods developed for tackling the multiple and conflicting objectives in in silico metabolic engineering, as well as the approaches in multiobjective optimization

Revelation of Yin-Yang Balance in Microbial Cell Factories by Data Mining, Flux Modeling, and Metabolic Engineering

The long-held assumption of never-ending rapid growth in biotechnology and especially in synthetic biology has been recently questioned, due to lack of substantial return of investment. One of the main reasons for failures in synthetic biology and metabolic engineering is the metabolic burdens that result in resource losses. Metabolic burden is defined as the portion of a host cells resources either energy molecules (e.g., NADH, NADPH and ATP) or carbon building blocks (e.g., amino acids) that is used to maintain the engineered components (e.g., pathways). As a result, the effectiveness of synthetic biology tools heavily dependents on cell capability to carry on the metabolic burden. Although genetic modifications can effectively engineer cells and redirect carbon fluxes toward diverse products, insufficient cell ATP powerhouse is limited to support diverse microbial activities including product synthesis. Here, I employ an ancient Chinese philosophy (Yin-Yang) to describe two contrary forces that are interconnected and interdependent, where Yin represents energy metabolism in the form of ATP, and Yang represents carbon metabolism. To decipher Yin-Yang balance and its implication to microbial cell factories, this dissertation applied metabolic engineering, flux analysis, data mining tools to reveal cell physiological responses under different genetic and environmental conditions. Firstly, a combined approach of FBA and 13C-MFA was employed to investigate several engineered isobutanol-producing strains and examine their carbon and energy metabolism. The result indicated isobutanol overproduction strongly competed for biomass building blocks and thus the addition of nutrients (yeast extract) to support cell growth is essential for high yield of isobutanol. Based on the analysis of isobutanol production, \u27Yin-Yang\u27 theory has been proposed to illustrate the importance of carbon and energy balance in engineered strains. The effects of metabolic burden and respiration efficiency (P/O ratio) on biofuel product were determined by FBA simulation. The discovery of energy cliff explained failures in bioprocess scale-ups. The simulation also predicted that fatty acid production is more sensitive to P/O ratio change than alcohol production. Based on that prediction, fatty acid producing strains have been engineered with the insertion of Vitreoscilla hemoglobin (VHb), to overcome the intracellular energy limitation by improving its oxygen uptake and respiration efficiency. The result confirmed our hypothesis and different level of trade-off between the burden and the benefit from various introduced genetic components. On the other side, a series of computational tools have been developed to accelerate the application of fluxomics research. Microbesflux has been rebuilt, upgraded, and moved to a commercial server. A platform for fluxomics study as well as an open source 13C-MFA tool (WUFlux) has been developed. Further, a computational platform that integrates machine learning, logic programming, and constrained programming together has been developed. This platform gives fast predictions of microbial central metabolism with decent accuracy. Lastly, a framework has been built to integrate Big Data technology and text mining to interpret concepts and technology trends based on the literature survey. Case studies have been performed, and informative results have been obtained through this Big Data framework within five minutes. In summary, 13C-MFA and flux balance analysis are only tools to quantify cell energy and carbon metabolism (i.e., Yin-Yang Balance), leading to the rational design of robust high-producing microbial cell factories. Developing advanced computational tools will facilitate the application of fluxomics research and literature analysis

Novel approach for integrated biomass supply chain synthesis and optimisation

Despite looming energy crises, fossil resources are still widely used for energy and chemical production. Growing awareness of the environmental impact from fossil fuels has made sustainability one of the main focuses in research and development. Towards that end, biomass is identified as a promising renewable source of carbon that can potentially replace fossil resources in energy and chemical productions. Although many researches on converting biomass to value-added product have been done, biomass is still considered underutilised in the industry. This is mainly due to challenges in the logistic and processing network of biomass. An integrated biomass supply chain synthesis and optimisation are therefore important. Thus, the ultimate goal of this thesis is to develop a novel approach for an integrated biomass supply chain. Firstly, a multiple biomass corridor (MBC) concept is presented to integrate various biomass and processing technologies into existing biomass supply chain system in urban and developed regions. Based on this approach, a framework is developed for the synthesis of a more diversified and economical biomass supply chain system. The work is then extended to consider the centralisation and decentralisation of supply chain structure. In this manner, P-graph-aided decomposition approach (PADA) is proposed, whereby it divides the complex supply chain problem into two smaller sub-problems – the processing network is solved via mixed-integer linear programming (MILP) model, whereas the binaries-intensive logistic network configuration is determined through P-graph framework. As existing works often focus on supply chain synthesis in urban regions with well-developed infrastructure, resources integrated network (RIN) – a novel approach for the synthesis of integrated biomass supply chain in rural and remote regions is introduced to enhance rural economies. This approach incorporates multiple resources (i.e. bioresources, food commodities, rural communities’ daily needs) into the value chain and utilises inland water system as the mode of transport, making the system more economically feasible. It extends the MBC approach for technology selection and adopts vehicle routing problem (VRP) for inland water supply and delivery network. To evaluate the performance of the proposed integrated biomass supply chain system, a FANP-based (fuzzy analytical network process) sustainability assessment tool is established. A framework is proposed to derive sustainability index (SI) from pairwise comparison done by supply chain stakeholders to assess the sustainability of a system. Fuzzy limits are introduced to reduce uncertainties in human judgment while conducting the pairwise comparison. To design a sustainable integrated biomass supply chain, a FANP-aided, a novel multiple objectives optimisation framework is proposed. This approach transforms multiple objective functions into single objective function by prioritising each of the objective through the FANP framework. The multiple objectives are then normalised via max-min aggregation to ensure the trade-off between objectives is performed on the same scale. At the end of this thesis, viable future works of the whole programme is presented for consideration