Miguel Mauricio-Iglesias

Challenges in industrial fermentation technology research

Industrial fermentation processes are increasingly popular, and are considered an important technological asset for reducing our dependence on chemicals and products produced from fossil fuels. However, despite their increasing popularity, fermentation processes have not yet reached the same maturity as traditional chemical processes, particularly when it comes to using engineering tools such as mathematical models and optimization techniques. This perspective starts with a brief overview of these engineering tools. However, the main focus is on a description of some of the most important engineering challenges: scaling up and scaling down fermentation processes, the influence of morphology on broth rheology and mass transfer, and establishing novel sensors to measure and control insightful process parameters. The greatest emphasis is on the challenges posed by filamentous fungi, because of their wide applications as cell factories and therefore their relevance in a White Biotechnology context. Computational fluid dynamics (CFD) is introduced as a promising tool that can be used to support the scaling up and scaling down of bioreactors, and for studying mixing and the potential occurrence of gradients in a tank.

Sensitivity analysis of autotrophic N removal by a granule based bioreactor: Influence of mass transfer versus microbial kinetics

A comprehensive and global sensitivity analysis was conducted under a range of operating conditions. The relative importance of mass transfer resistance versus kinetic parameters was studied and found to depend on the operating regime as follows: Operating under the optimal loading ratio of 1.90(gO(2)/m(3)/d)/(gN/m(3)/d), the system was influenced by mass transfer (10% impact on nitrogen removal) and performance was limited by AOB activity (75% impact on nitrogen removal), while operating above, AnAOB activity was limiting (68% impact on nitrogen removal). The negative effect of oxygen mass transfer had an impact of 15% on nitrogen removal. Summarizing such quantitative analyses led to formulation of an optimal operation window, which serves a valuable tool for diagnosis of performance problems and identification of optimal solutions in nitritation/anammox applications.

Development of novel control strategies for single-stage autotrophic nitrogen removal: A process oriented approach

Abstract The autotrophic nitrogen removing granular sludge process is a novel and intensified process. However, its stable operation and control remain a challenging issue. In this contribution, a process oriented approach was used to develop, evaluate and benchmark novel control strategies to ensure stable operation and rejection of disturbances. Three novel control strategies were developed, evaluated, and benchmarked against each other: a feedforward control (control structure 1 – CS#1), a rule-based feedback control (CS#2), and a feedforward–feedback controller, in which the feedback loop updates the set point of the feedforward loop (CS#3). The CS#1 gave the best performance against disturbances in the ammonium concentration, whereas the CS#2 provided the best performance against disturbances in the organic carbon concentration and dynamic influent conditions. The CS#3 rejected both disturbances satisfactorily. Thus, the appropriate design will depend on the specific disturbances in the influent generated in the upstream units of the wastewater treatment plant.

pH variation and influence in an autotrophic nitrogen removing biofilm system using an efficient numerical solution strategy

A pH simulator consisting of an efficient numerical solver of a system of nine nonlinear equations was constructed and implemented in the modeling software MATLAB. The pH simulator was integrated in a granular biofilm model and used to simulate the pH profiles within granules performing the nitritation-anammox process for a range of operating points. The simulation results showed that pH profiles were consistently increasing with increasing depth into the granule, since the proton-producing aerobic ammonium-oxidizing bacteria (AOB) were located close to the granule surface. Despite this pH profile, more NH3 was available for AOB than for anaerobic ammonium oxidizers, located in the center of the granules. However, operating at a higher oxygen loading resulted in steeper changes in pH over the depth of the granule and caused the NH3 concentration profile to increase from the granule surface towards the center. The initial value of the background charge and influent bicarbonate concentration were found to greatly influence the simulation result and should be accurately measured. Since the change in pH over the depth of the biofilm was relatively small, the activity potential of the microbial groups affected by the pH did not change more than 5% over the depth of the granules.

A generic methodology for the optimisation of sewer systems using stochastic programming and self-optimizing control

The design of sewer system control is a complex task given the large size of the sewer networks, the transient dynamics of the water flow and the stochastic nature of rainfall. This contribution presents a generic methodology for the design of a self-optimising controller in sewer systems. Such controller is aimed at keeping the system close to the optimal performance, thanks to an optimal selection of controlled variables. The definition of an optimal performance was carried out by a two-stage optimisation (stochastic and deterministic) to take into account both the overflow during the current rain event as well as the expected overflow given the probability of a future rain event. The methodology is successfully applied to design an optimising control strategy for a subcatchment area in Copenhagen. The results are promising and expected to contribute to the advance of the operation and control problem of sewer systems.

Role of methanogenesis on the biotransformation of organic micropollutants during anaerobic digestion

Several studies showed that some organic micropollutants (OMPs) are biotransformed during anaerobic digestion (AD). Yet, most of them aim at reporting removal efficiencies instead of understanding the biotransformation process. Indeed, how each of the main AD stages (i.e., hydrolysis, acidogenesis, and methanogenesis) contribute to OMP biotransformation remains unknown. This study focuses on investigating the role of methanogenesis, the most characteristic step of AD, to OMP removal. More specifically, the sorption and the biotransformation of 20 OMPs by methanogenic biomass were analyzed determining their concentrations in both liquid and solid phases. Sorption onto methanogenic biomass displayed a similar behavior as reported for digested sludge. Most of the OMPs were biotransformed to a medium extent (35-70%) and only sulfamethoxazole was completely removed. Comparing these results with those reported for the complete AD process, methanogenesis was proven to play a key role, accounting for more than 50% of the OMP biotransformation (except for roxithromycin) during AD. An increase in the organic loading rate from 1 to 2gCOD/Ld, typical loads employed in sewage sludge anaerobic digesters, did not exert a clear cometabolic effect on the OMPs biotransformation. It is hypothesized that biotransformation occurs in both liquid and solid phases because no link between the partition coefficient (Kd) and the overall biotransformation efficiency was found. These findings allow a better understanding of the OMPs fate under anaerobic conditions, which is necessary to design efficient biological mitigation strategies.

A fuzzy-logic based diagnosis and control of a reactor performing complete autotrophic nitrogen removal

Abstract This contribution explores the use of diagnosis and control modules based on fuzzy set theory and logic for bioreactor monitoring and control. With this aim, two independent modules were used jointly to carry out first the diagnosis of the state of the system and then use transfer this information to control the reactor. The separation in diagnosis and control allowed a more intuitive design of the membership functions and the production rules. Hence, the resulting diagnosis-control module is simple to tune, update and maintain while providing a good control performance. In particular the diagnosis-control system was designed for a complete autotrophic nitrogen removal process. The whole module is evaluated by dynamic simulation. Additionally, the diagnosis tool was demonstrated by analysis 100 days of experimental data.

Control of a Biological Nitrogen Removal Process in an Intensified Single Reactor Configuration

Abstract The nitrogen removing granular sludge process is a novel and intensified process. However, its stable operation and control remains a challenging problem. In this contribution, a new process oriented approach is used to develop, evaluate and benchmark control strategies to ensure stable operation and rejection of disturbances. Three control strategies were developed: a feedforward control (case 1), a rule-based feedback control (case 2), and a feedforward-feedback controller, in which the feedback loop updates the set point of the feedforward loop (case 3). The case 1 controller, based on influent measurements, was giving the best performance against disturbances in the ammonium concentration, whereas case 2 was providing the best performance against disturbances in the organic carbon concentration. The case 3 controller rejected both disturbances satisfactorily. Thus, this controller provided versatility towards disturbance rejection, however through a less tight control, which meant a bigger offset from the removal efficiency.

A systematic methodology for the robust quantification of energy efficiency at wastewater treatment plants featuring Data Envelopment Analysis

This article examines the potential benefits of using Data Envelopment Analysis (DEA) for conducting energy-efficiency assessment of wastewater treatment plants (WWTPs). WWTPs are characteristically heterogeneous (in size, technology, climate, function …) which limits the correct application of DEA. This paper proposes and describes the Robust Energy Efficiency DEA (REED) in its various stages, a systematic state-of-the-art methodology aimed at including exogenous variables in nonparametric frontier models and especially designed for WWTP operation. In particular, the methodology systematizes the modelling process by presenting an integrated framework for selecting the correct variables and appropriate models, possibly tackling the effect of exogenous factors. As a result, the application of REED improves the quality of the efficiency estimates and hence the significance of benchmarking. For the readers convenience, this article is presented as a step-by-step guideline to guide the user in the determination of WWTPs energy efficiency from beginning to end. The application and benefits of the developed methodology are demonstrated by a case study related to the comparison of the energy efficiency of a set of 399 WWTPs operating in different countries and under heterogeneous environmental conditions.

Measuring Energy Demand and Efficiency at WWTPs: An Econometric Approach

As the number of wastewater treatment plants (WWTPs) increases worldwide and the effluent quality requirements become more demanding, the issue of energy efficiency has been attracting increasing attention from an environmental and economic point of view. However, defining and measuring energy efficiency in WWTPs is still a challenge. Energy efficiency is typically approximated by energy intensity, i.e. kWh/m3. However WWTPs can perform different functions (i.e. removing of COD, removing of N and/or P, resource recovery, producing an effluent free of pathogens), or perform the same function with different technologies, making the comparison of WWTPs a challenging task. Thus, common energy intensity indicators have limited value, as they do not provide enough information of the WWTPs operation. Furthermore, changes in energy intensity are just approximate indicators for changes in energy efficiency since they are affected by external (exogenous) factors.