Development of a novel electrochemical system for oxygen control (ESOC) to examine dissolved oxygen inhibition on algal activity.

The development of an Electrochemical System for Oxygen Control (ESOC) for examining algal photosynthetic activity as a function of dissolved oxygen (DO) is outlined. The main innovation of the tool is coulombic titration in order to balance the electrochemical reduction of oxygen with the oxygen input to achieve a steady DO set-point. ESOC allows quantification of algal oxygen production whilst simultaneously maintaining a desired DO concentration. The tool was validated abiotically by comparison with a mass transfer approach for quantifying oxygenation. It was then applied to quantify oxygen inhibition of algal activity. Five experiments, using an enriched culture of Scenedesmus sp. as the inoculum, are presented. For each experiment, ESOC was used to quantify algal activity at a series of DO set-points. In all experiments substantial oxygen inhibition was observed at DO >30 mgO2 L-1. Inhibition was shown to fit a Hill inhibition model, with a common Hill coefficient of 0.22±0.07 L mg-1 and common log10 CI50 of 27.2±0.7 mg L-1. This is the first time that the oxygen inhibition kinetic parameters have been quantified under controlled DO conditions.

Deep learning in wastewater treatment: a critical review.

Modeling wastewater processes supports tasks such as process prediction, soft sensing, data analysis and computer assisted design of wastewater systems. Wastewater treatment processes are large, complex processes, with multiple controlling mechanisms, a high degree of disturbance variability and non-linear (generally stable) behavior with multiple internal recycle loops. Semi-mechanistic biochemical models currently dominate research and application, with data-driven deep learning models emerging as an alternative and supplementary approach. But these modeling approaches have grown in separate communities of research and practice, and so there is limited appreciation of the strengths, weaknesses, contrasts and similarities between the methods. This review addresses that gap by providing a detailed guide to deep learning methods and their application to wastewater process modeling. The review is aimed at wastewater modeling experts who are familiar with established mechanistic modeling approach, and are curious about the opportunities and challenges afforded by deep learning methods. We conclude with a discussion and needs analysis on the value of different ways of modeling wastewater processes and open research problems.

High pressure thermal hydrolysis as pre-treatment to increase the methane yield during anaerobic digestion of microalgae.

Anaerobic digestion of algal biomass will be an essential component of algal biofuel production systems, yet the methane yield from digestion of algae is typically much lower than the theoretical potential. In this work, high pressure thermal hydrolysis (HPTH) is shown to enhance methane yield during algae digestion. HPTH pre-treatment was applied to both raw algae and algal residue resulting from lipid extraction. HPTH and even the lipid extraction process itself increased methane yield, by 81% and 33% respectively; in combination they increased yield by 110% over that of the raw algae (18L CH4 gVS(-1) substrate). HPTH had little effect on the rate of anaerobic digestion, however lipid extraction enhanced it by 33% over that for raw algae (0.21day(-1)). Digestion resulted in solubilisation of nitrogen (and phosphorous to a lesser degree) in all cases, showing that there is potential for nutrient recycling for algal growth.