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A key approach in expediting the transition towards a bio-based economy is the conceptual design and implementation of integrated second-generation biorefineries (iSGB) [1]. Despite tremendous efforts in research as well as past economic and political initiatives, the number of active iSGBs worldwide is dramatically low, mainly due to their deficient economic robustness [2]. A conceptual design approach for these iSGBs is Superstructure Optimization (SSO), which yields an optimal candidate process topology (CPT), but which is inherently limited by the initial search space and the fidelity of the unit operation models [3,4]. This contrasts highly with the complexity of fermentation processes and disregards recent advances in synthetic biology for the optimization of the cell factories employed in these processes [2]. From a biotechnological perspective, this hurdle is surmounted by a design approach, in which the products are specified a priori and subsequently the cell factory and the process are tailored towards the product and finally the feedstock is specified, thus "having the end in mind" [5,6]. Furthermore, by following a simulation-based optimization (SBO) approach, complex models including physiological and operational specifications of cell factories can be employed in the search for an optimal process design. Despite being computationally demanding, the second benefit from it is the possibility to include uncertainties into the process design with models of complex biological systems [7]. In this work, we therefore propose a novel synergistic framework for the synthesis and design of iSGBs: based on a hybrid approach integrating surrogate-based superstructure optimization (SSO) with simulation-based design optimization (SBO), it harnesses both the power of the SSO for process synthesis and the potential of SBO for detailed design optimization. The framework itself capitalizes thorough knowledge regarding biotechnology and synthetic biology in order to guide decisions for both SSO and SBO, which results in a consolidated framework and an expedited evaluation process.

Biorefinery is a promising concept that can contribute overcoming the petrol-era, especially with respect to sustainable fine chemical production, addressing at the same time several problems: the depletion of petroleum resources (with the associated consequences), human sustainability, waste management and political concerns 1,2. Production and separation of valuable products from biomass have indeed been successfully achieved and implemented at full scale3. However, the lack of cost-effective downstream processes is largely preventing biorefinery products to become economically competitive, and membranes are one of the fundamental technologies for separation of fermentation products such as succinic acid4 (SA). Therefore, key factors such as pH, pressure, steric effect etc. in downstream processes must be identified for a technological breakthrough. Data collection about different feedstocks, fermentation and downstream techniques for bio-SA production will highlight the most relevant for large-scale application. These, will be then study trough a techno-economical analysis, which will be focused on membrane separation techniques. Thus, a computer-aided framework will be used to assess and rank the critical parameters in downstream technologies, which will be subsequently tested trough an experimental validation of bio-SA production. The interest for bio-SA production have been constantly increasing3,5, since more than 30 commercially valuable products can be currently synthetized from it, including solvents and lubricants, synthetic resins and biodegradable polymers such as PBS and polyamides, cosmetics, food and pharmaceuticals 3,5. Finally, a defined and interactive operation range for each studied variable is intended to be provided, which can be virtually extrapolated to other similar separation processes. The feasibility of potential alternatives will be evaluated experimentally on other similar processes. 1

The goal of this paper is to review and critically assess different methods to monitor key process variables for ethanol production from lignocellulosic biomass. Because cellulose-based biofuels cannot yet compete with noncellulosic biofuels, process control and optimization are of importance to lower the production costs. This study reviews different monitoring schemes, to indicate what the added value of real-time monitoring is for process control. Furthermore, a comparison is made on different monitoring techniques to measure the off-gas, the concentrations of dissolved components in the inlet to the process, the concentrations of dissolved components in the reactor, and the biomass concentration. Finally, soft sensor techniques and available models are discussed, to give an overview of modeling techniques that analyze data, with the aim of coupling the soft sensor predictions to the control and optimization of cellulose to ethanol fermentation. The paper ends with a discussion of future needs and developments ; This work was partially financed by the European Regional Development Fund (ERDF) and Region Zealand (Denmark) through the BIOPRO-SMV project. Furthermore, the work received funding from Innovation Fund Denmark (BIOPRO2 strategic research center, project number 4105-00020B). This project has also been supported partially by the EUDP project 'Demonstration of 2G ethanol in full scale, MEC' (Jr. no. 64015–0642). Finally, we wish to acknowledge the support obtained from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement number 713683 (COFUNDfellowsDTU) and from the Danish Council for Independent Research in the frame of the DFF FTP research project GREENLOGIC (grant agreement number 7017-00175A). Miguel Mauricio-Iglesias belongs to the Galician Competitive Research Group GRC2013-032 and the CRETUS strategic partnership (AGRUP2015/02), co-funded by FEDER (EU) ; SI

Operating lignocellulosic fermentation processes to produce fuels and chemicals is challenging due to the inherent complexity and variability of the fermentation media. Real‐time monitoring is necessary to compensate for these challenges, but the traditional process monitoring methods fail to deliver actionable information that can be used to implement advanced control strategies. In this study, a hybrid‐modeling approach is presented to monitor cellulose‐to‐ethanol (EtOH) fermentations in real‐time. The hybrid approach uses a continuous‐discrete extended Kalman filter to reconciliate the predictions of a data‐driven model and a kinetic model and to estimate the concentration of glucose (Glu), xylose (Xyl), and EtOH. The data‐driven model is based on partial least squares (PLS) regression and predicts in real‐time the concentration of Glu, Xyl, and EtOH from spectra collected with attenuated total reflectance mid‐infrared spectroscopy. The estimations made by the hybrid approach, the data‐driven models and the internal model were compared in two validation experiments showing that the hybrid model significantly outperformed the PLS and improved the predictions of the internal model. Furthermore, the hybrid model delivered consistent estimates even when disturbances in the measurements occurred, demonstrating the robustness of the method. The consistency of the proposed hybrid model opens the doors towards the implementation of advanced feedback control schemes ; This project has been partially supported by the Energy Technology Development and Demonstration Program in the frame of the project "Demonstration of 2G ethanol production in full scale" (Grant Number 64015‐0642), and has been realized together with Maabjerg Energy Center. The support of the BIOPRO2 Strategic Research Center (Grant Agreement No. 4105‐00020B) is gratefully acknowledged. The authors wish to acknowledge the support provided by the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska‐Curie Grant Agreement ...

Furfural (FF) and 5-hydroxymethylfurfural (HMF) are furan derivatives commonly generated during the pretreatment of lignocellulosic biomass and often considered among the most inhibitory compounds towards the sugar fermenting strains due to their acute toxicity and high concentrations. The present study describes the simultaneous detoxification and fermentation of lignocellulosic hydrolysates containing high concentrations of FF and HMF by a co-culture of a novel Fusarium striatum strain and a xylose consuming Saccharomyces cerevisiae strain. The process demonstrates a superior performance than those previously described in the literature, as FF and HMF were efficiently transformed into their less toxic added-value alcohol derivatives by F. striatum with high yields (99% and 86%, respectively) and the higher detoxification rates reported (0.56 g/L/h and 0.13 g/L/h, respectively). There was no sugar consumption by the filamentous fungus during the detoxification process, rendering it available for ethanol fermentation by S. cerevisiae, which started immediately after the detoxification of the inhibitors. Ethanol productivities were significantly higher when increasing the inoculum size of F. striatum, confirming its potential for the detoxification of the lignocellulosic hydrolysate. High ethanol yields (0.4 g/g) and productivities (0.46 g/L/h) were obtained in a bench-scale bioreactor (1.5 L) in the presence of 3.5 g/L HMF and 2.5 g/L FF, a concentration of furan derivatives that completely inhibited the fermentation process in the absence of F. striatum. The presented process allows access to lignocellulosic materials and pretreatment methods that result in high concentrations of FF and HMF that are currently not feasible, representing a significant advance for the lignocellulosic ethanol industry. ; This work was partially supported by the Spanish Government (PID2019-110735RB-C21, MICIN/FEDER) and the Catalan Government (FI_B1_00135). The support of the BIOPRO2 Strategic Research Center (Grant Agreement No. 4105-00020B) is gratefully acknowledged.

The use of process models to simulate the fate of micropollutants in wastewater treatment plants is constantly growing. However, due to the high workload and cost of measuring campaigns, many simulation studies lack sufficiently long time series representing realistic wastewater influent dynamics. In this paper, the feasibility of the Benchmark Simulation Model No. 2 (BSM2) influent generator is tested to create realistic dynamic influent (micro)pollutant disturbance scenarios. The presented set of models is adjusted to describe the occurrence of three pharmaceutical compounds and one of each of its metabolites with samples taken every 2–4 h: the anti-inflammatory drug ibuprofen (IBU), the antibiotic sulfamethoxazole (SMX) and the psychoactive carbamazepine (CMZ). Information about type of excretion and total consumption rates forms the basis for creating the data-defined profiles used to generate the dynamic time series. In addition, the traditional influent characteristics such as flow rate, ammonium, particulate chemical oxygen demand and temperature are also modelled using the same framework with high frequency data. The calibration is performed semi-automatically with two different methods depending on data availability. The 'traditional' variables are calibrated with the Bootstrap method while the pharmaceutical loads are estimated with a least squares approach. The simulation results demonstrate that the BSM2 influent generator can describe the dynamics of both traditional variables and pharmaceuticals. Lastly, the study is complemented with: 1) the generation of longer time series for IBU following the same catchment principles; 2) the study of the impact of in-sewer SMX biotransformation when estimating the average daily load; and, 3) a critical discussion of the results, and the future opportunities of the presented approach balancing model structure/calibration procedure complexity versus predictive capabilities ; The authors acknowledge the People Program (Marie Curie Actions) of the European Union's ...