Abstract. In this work, Evolutionary Algorithms (EAs) are used to achieve optimal feedforward control in a recombinant bacterial fed-batch fermentation process, that aims at producing a bio-pharmaceutical product. Three different aspects are the target of the optimization procedure: the feeding trajectory (the amount of substrate introduced in a bioreactor per time unit), the duration of the fermentation and the initial conditions of the process. A novel EA with variable size chromosomes and using real-valued representations is proposed that is capable of simultaneously optimizing the aforementioned aspects. Outstanding productivity levels were achieved and the results are validated by practice.

In the present work the characterisation by image analysis of anaerobic biomass under organic and hydraulic shocks was performed. The digester was fed with a synthetic substrate, containing 50% oleic acid (as COD). Organic and hydraulic shocks were performed by stepwise increasing the substrate concentration or by reducing the hydraulic retention time. In both cases the organic loading rate changed from 6 to 30 kg COD/m3.d. Hydraulic shock induced a fast decrease in the number of free filaments and in total filament length, which attained a minimum value 51 hours after beginning the shock. The initial filament values were not recovered 340 hours after the hydraulic shock. In the organic shock, the minimum values of these parameters were detected 200 hours after beginning the shock and initial values were recovered 840 hours after. During the hydraulic shock the methanogenic acetoclastic activity was directly correlated to the number and length of free filaments. This result suggests that filaments are predominantly acetoclastic bacteria, probably Methanosaeta.

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The specific growth rate is one of the most important process variables characterizing the state of microorganisms during fermentations mainly because the biosynthesis of many products of interest is often related with the values assumed by this parameter. In the particular case of the fed-batch operation of Escherichia coli for the production of recombinant proteins, it is important to maintain the specific growth rate below a certain threshold in order to avoid the accumulation of acetic acid throughout the fermentation and, additionally, it is often argued that both pre-and the post-induction specific growth rates should be closely controlled in order to achieve maximum productivities of the desired recombinant protein. In a previous work the authors have developed and validated by simulations a strategy for the automatic control of the specific growth rate in E. coli fed-batch fermentations based on an asymptotic observer for biomass and on developed estimators for the specific growth rates. The main purpose of the present work was to implement experimentally the developed observer, estimator and controller in a real fed-batch fermentation process. For that purpose a data acquisition and control program was developed in LabVIEW that allows the acquisition of the necessary on line data (off gas and dissolved oxygen concentration and culture weight) and the calculation of the feeding rates using the developed equations. The feedforward-feedback controller developed was able to keep the culture growing in an exponential phase throughout the fermentation without accumulation of glucose and acetate.

The growing presence of pharmaceutical compounds in aqueous systems leads to the search for new efficient and eco-friendly solutions to this problem. Although pharmaceutical products are widely found in the aqueous environments, there is limited understanding of their ecological effects. To augment the removal of pharmaceuticals, a bench reactor was inoculated with activated sludge and fed with a synthetic medium. Two pharmaceuticals of widespread usage, ibuprofen (IBU) and paracetamol (PARA) in the range 0.4-1 mg L -1 were used. The uptake values increased from 0.192 to 0.660 mg g -1 for IBU and from 0.104 to 0.341 mg g -1 for PARA. The removal efficiency reached values from 99.1-99.5 % and is independent of the initial IBU concentration. For PARA the removal percentage ranged from 93.3-98.8 decreasing with the increase on the initial concentration. The removal mechanism is well described by pseudo-first order and pseudo-second order kinetic models for all concentrations tested. At same time, batch assays were performed in order to assess the toxicity of both pharmaceuticals into the activated sludge using quantitative image analysis (QIA). For IBU experiments, QIA studies showed that this compound favors the growth of aggregated biomass rather than filamentous bacteria. A comparison between this biological technology and adsorption by a commercial porous ceramic material and Pinus bark was also performed. The results showed that the biological technology allowed better results (99.5 % against 19.4 and 9.3 %, respectively for the ceramic material and Pinus bark) for IBU. The results obtained for PARA showed comparable results for the biological technology and for adsorption by the ceramic material. The activated sludge system presented here appears to be a promising treatment for pharmaceutical contaminated effluents and the biomass present in the activated sludge seems do not be negatively affected by the presence of high concentrations of both pharmaceuticals.

This paper describes the optimal design and operation of an activated sludge system in wastewater treatment plants. The optimization problem is represented as a smooth programming problem with linear and nonlinear equality and inequality constraints, in which the objective is to minimize the total cost required to design and operate the activated sludge system under imposed effluent quality laws. We analyze four real world plants in the Trás-os-Montes region (Portugal) and report the numerical results obtained with the FILTER, IPOPT, SNOPT and LOQO optimizers.

Abstract-The optimization of biotechnological processes is a complex problem that has been intensively studied in the past few years due to the economic impact of the products obtained from fermentations.In fed-batch processes, the goal is to find the optimal feeding trajectory that maximizes the final productivity. Several methods, including Evolutionary Algorithms (EAs) have been applied to this task in a number of different fermentation processes.This paper performs an experimental comparison between Particle Swarm Optimization, Differential Evolution and a real-valued EA in three distinct case studies, taken from previous work by the authors and literature, all considering the optimization of fed-batch fermentation processes.

Genome-scale metabolic models have been recognized as useful tools for better understanding living organism's metabolism. Merlin (https://merlin-sysbio.org/) is an open-source and user-friendly resource that hastens these models' reconstruction process, conjugating manual, and automatic procedures, while leveraging user's expertise with a curation-oriented graphical interface. An updated and redesigned version of merlin is herein presented. Since 2015, several features were implemented in merlin, along with profound changes in the software architecture, operating flow, and graphical interface. The current version (4.0) includes the implementation of novel algorithms and third-party tools for genome functional annotation, draft assembly, model refinement, and curation. Such updates led to an increase in the user-base, resulting in multiple published works including genome metabolic (re-)annotation and model reconstruction of multiple (lower and higher) eukaryotes and prokaryotes.