Characterisation of carbon fixation proteins in the macroalgal biomass feedstock, Ulva spp.

Ulva spp. is a macroalgae widely distributed and abundant in nature, however, its exploitation as a biofuel feedstock has been scarce due the lack of information about its metabolic functioning. On the other hand, microalgae have been extensively studied allowing understanding of the complexity of these organisms and, at the same time, providing a guide which could be extrapolated to macroalgal metabolic systems. As an essential metabolic process, the study of carbon dioxide fixation and associated intracellular structures is fundamental in order to improve and engineer changes in algal biomass yield. As part of these intracellular components, the pyrenoid is a microcompartment where carbon dioxide is fixed by maintaining a CO2 rich environment around Rubisco. Although Rubisco is the main constituent of the pyrenoid matrix, it is not the only one and it is not efficient enough to carry out CO2 fixation by itself. Previous work in the model microalga Chlamydomonas reinhardtii has revealed the existence of a linker protein called EPYC1, whose main role is to bind Rubisco together to form a complex avoiding CO2 leakage. To study pyrenoid in Ulva, a chloroplast isolation method was developed. Bioinformatics searches were performed in order to find a putative protein in the Ulva proteome with similar physicochemical properties to EPYC1. A single target candidate which fulfilled all physicochemical properties was identified. Finding of a putative of Ulva EPYC1-like protein allows for further studies.

Novel analysis and modelling methodologies applied to pultrusion and other processes

Often a manufacturing process may be a bottleneck or critical to a business. This thesis focuses on the analysis and modelling of such processest, to both better understand them, and to support the enhancement of quality or output capability of the process. The main thrusts of this thesis therefore are: To model inter-process physics, inter-relationships, and complex processes in a manner that enables re-exploitation, re-interpretation and reuse of this knowledge and generic elements e.g. using Object Oriented (00) & Qualitative Modelling (QM) techniques. This involves the development of superior process models to capture process complexity and reuse any generic elements; To demonstrate advanced modelling and simulation techniques (e.g. Artificial Neural Networks(ANN), Rule-Based-Systems (RBS), and statistical modelling) on a number of complex manufacturing case studies; To gain a better understanding of the physics and process inter-relationships exhibited in a number of complex manufacturing processes (e.g. pultrusion, bioprocess, and logistics) using analysis and modelling. To these ends, both a novel Object Oriented Qualitative (Problem) Analysis (OOQA) methodology, and a novel Artificial Neural Network Process Modelling (ANNPM) methodology were developed and applied to a number of complex manufacturing case studies- thermoset and thermoplastic pultrusion, bioprocess reactor, and a logistics supply chain. It has been shown that these methodologies and the models developed support capture of complex process inter-relationships, enable reuse of generic elements, support effective variable selection for ANN models, and perform well as a predictor of process properties. In particular the ANN pultrusion models, using laboratory data from IKV, Aachen and Pera, Melton Mowbray, predicted product properties very well

Processing Modeling of Hot Air Convective Drying of Sugar Kelp (Saccharina Latissima)

Recent interest among consumers to avoid added chemical additives/preservatives has led to the recognition of seaweed as a healthy source of fibers, minerals, and antioxidants. Currently, global seaweed aquaculture is valued over US$ 6 billion and is increasing at a steady rate of 8% annually. Moreover, as per NOAA Fisheries the US imports more than 80% of the seafood consumed. This provides huge economic and workforce development opportunities in the seaweed aquaculture industry of Maine. Consequently, farming sugar kelp (Saccharina latissima), a brown seaweed, is gaining momentum along the northeast US coast. Due to its seasonal availability and limited shelf life, seaweeds are sun-dried or using hot-air to remove moisture, preventing oxidation and microbial growth. The goal of this research is to solve the bottlenecks of drying seaweed in Maine by developing an innovative technology focused on a clean, energy-efficient and closed drying system for producing top-notch and local finished products for American consumers. For this project, the effect of drying and storage conditions (temperature, humidity) on the physical, chemical and thermal properties of the final product are studied. Also, a mathematical drying model is developed to understand the drying kinetics and rate of moisture removal in hot-air driers. Investigations carried out throughout this experiment shows controlled environment drying can improve the predictability of drying dynamics significantly for the preservation of health-beneficial components in sugar kelp. The developed model showed drying can be optimized to create a carbon negative and sustainable seaweed processing industry in Maine

Investigative Study of Microalgal and Electrochemical Wastewater Treatment Systems and Modeling of the Wafer-Enhanced Electrodeionization Using Supervised Learning

Wastewater has a serious impact on environment and public health due to its high concentration of nutrients and toxic contaminants. Without proper treatment, excess nutrients discharged in wastewater can cause a damage to the ecosystem such as undesirable pH shifts, cyanotoxin production, and low dissolved oxygen concentrations. Main objectives of this dissertation work were to investigate i) the biofuel potential of P. cruentum when grown in swine wastewater, ii) the influence of four most commonly used ion exchange resins on the system efficiency and selectivity for the removal of sodium, calcium, and magnesium ions, and iii) the modeling of wafer-enhanced electrodeionization with data science and machine learning techniques. The growth and lipid production of the microalgae Porphyridium (P.) cruentum grown in swine wastewater (ultra-filtered and raw) were examined as compared with control media (L−1, modified f/2) at two different salt concentrations (seawater and saltwater). The cultivation of P. cruentum in the treated swine wastewater media (seawater = 5.18 ± 2.3 mgl−1day−1, saltwater = 3.32 ± 1.93 mgl−1day−1) resulted in a statistically similar biomass productivity compared to the control medium (seawater = 2.61 ± 2.47 mgl−1day−1, saltwater = 6.53 ± 0.81 mgl−1day−1) at the corresponding salt concentration. Furthermore, no major differences between the fatty acid compositions of microalgae in the treated swine wastewater medium and the control medium were observed. The performance comparison of four commonly used cation exchange resins (Amberlite IR120 Na+, Amberlite IRP 69, Dowex MAC 3 H+, and Amberlite CG 50) and their influence on the current efficiency and selectivity for the removal of cations from a highly concentrated salt stream were also reported in this work. The current efficiencies were high for all the resin types studied. Results also revealed that weak cation exchange resins favor the transport of the monovalent ion (Na+) while strong cation exchange resins either had no strong preference or preferred to transport the divalent ions (Ca2+ and Mg2+). Moreover, the strong cation exchange resins in powder form generally performed better in wafers than those in the bead form for the selective removal of divalent ions (selectivity \u3e 1). To further understand the impact of particle size, resins in the bead form were ground into a powder. After grinding the strong cation resins displayed similar behavior (more consistent current efficiency and preference for transporting divalent ions) to the strong cation resins in powder form. This indicates the importance of resin size in the performance of wafers. Through this research, the modeling of wafer-enhanced electrodeionization with high concentration multi-ion solution has been accomplished. This paper is the first study that uses data science and machine learning techniques for the modeling of wafer-enhanced electrodeionization with high concentration multi-ion solutions. With the use of data science and machine learning, the sodium, calcium, and magnesium ion concentrations were predicted with multioutput regression and neural networks multilayer perceptron (NN-MLP), and the observed effects of different resin wafers were confirmed using both multioutput and single output regression as well as leave-one-out cross validation and NN-MLP

Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management

The main aim of this book is to present various implementations of ML methods and metaheuristic algorithms to improve modelling and prediction hydrological and water resources phenomena having vital importance in water resource management