36 results found
Pedersen A, Pandya J, Leonzio G, et al., 2023, Comparative techno-economic and life-cycle analysis of precious versus non-precious metal electrocatalysts: the case of PEM fuel cell cathodes, Green Chemistry, Vol: 25, Pages: 10458-10471, ISSN: 1463-9262
Sluggish kinetics in the oxygen reduction reaction (ORR) require significant quantities of expensive Pt-based nanoparticles on carbon (Pt/C) at the cathode of proton exchange membrane fuel cells (PEMFCs). This catalyst requirement hinders their large-scale implementation. Single atom Fe in N-doped C (Fe-N-C) electrocatalysts offer the best non-Pt-based ORR activities to date, but their environmental impacts have not been studied and their production costs are rarely quantified. Herein, we report a comparative life-cycle assessment and techno-economic analysis of replacing Pt/C with Fe-N-C at the cathode of an 80 kW PEMFC stack. In the baseline scenario (20 gPt/Cvs. 690 gFe-N-C), we estimate that Fe-N-C could reduce damages on ecosystems and human health by 88-90% and 30-44%, respectively, while still increasing global warming potential by 53-92% and causing a comparable impact on resource depletion. The environmental impacts of Pt/C predominantly arise from the Pt precursor while those of Fe-N-C are presently dominated by the electricity consumption. The monetized costs of environmental externalities for both Fe-N-C and Pt/C catalysts exceed their respective direct production costs. Based on catalyst performance with learning curve analysis at 500 000 PEMFC stacks per annum, we estimate replacing Pt/C with Fe-N-C would increase PEMFC stack cost from 13.8 to 41.6 USD per kW. The cost increases despite a reduction in cathode catalyst production cost from 3.41 to 0.79 USD per kW (excluding environmental externalities). To be cost-competitive with a Pt-based PEMFC stack delivering 2020 US Department of Energy target of 1160 mW cm−2 (at 0.657 V), the same stack with an Fe-N-C cathode would need to reach 874 mW cm−2, equivalent to a 200% performance improvement. These findings demonstrate the need for continued Fe-N-C activity development with sustainable synthesis routes in mind to replace Pt-based cathode catalyst in PEMFCs. Based on forecasting scenarios of
Baaqel HA, Bernardi A, Hallett JP, et al., 2023, Global sensitivity analysis in life-cycle assessment of early-stage technology using detailed process simulation: application to dialkylimidazolium ionic liquid production., ACS Sustainable Chemistry and Engineering, Vol: 11, Pages: 7157-7169, ISSN: 2168-0485
The ability to assess the environmental performance of early-stage technologies at production scale is critical for sustainable process development. This paper presents a systematic methodology for uncertainty quantification in life-cycle assessment (LCA) of such technologies using global sensitivity analysis (GSA) coupled with a detailed process simulator and LCA database. This methodology accounts for uncertainty in both the background and foreground life-cycle inventories, and is enabled by lumping multiple background flows, either downstream or upstream of the foreground processes, in order to reduce the number of factors in the sensitivity analysis. A case study comparing the life-cycle impacts of two dialkylimidazolium ionic liquids is conducted to illustrate the methodology. Failure to account for the foreground process uncertainty alongside the background uncertainty is shown to underestimate the predicted variance of the end-point environmental impacts by a factor of two. Variance-based GSA furthermore reveals that only few foreground and background uncertain parameters contribute significantly to the total variance in the end-point environmental impacts. As well as emphasizing the need to account for foreground uncertainties in LCA of early-stage technologies, these results illustrate how GSA can empower more reliable decision-making in LCA.
Yang Z, Ahmad S, Bernardi A, et al., 2023, Evaluating alternative low carbon fuel technologies using a stakeholder participation-based q-rung orthopair linguistic multi-criteria framework, Applied Energy, Vol: 332, ISSN: 0306-2619
It is widely believed that alternative low carbon fuels (ALCF) can be instrumental in achieving the transportation sector's decarbonization goal. Unlike conventional fossil-based fuels, ALCF can be produced through a combination of different chemical processes and feedstocks. The inherent complexity of the problem justifies the multi-criteria decision-making (MCDM) approach to support decision-making in the presence of multiple criteria and data uncertainty. In this paper, we propose a novel stakeholder participation-based MCDM framework integrating experts' perspectives on ALCF production pathways using the analytics hierarchy process (AHP) and the q-rung orthopair linguistic partition Bonferroni mean (q-ROLPBM) operator. The key merit of our approach lies in treating criteria of different dimensions as heterogeneous indicators while considering the mutual influence between criteria within the same dimension. The proposed framework is applied to evaluate four ALCF production pathways against 13 criteria categorised under economic, environmental, technical, and social dimensions for the case of the United Kingdom (UK). Our analysis revealed the environmental and the economic dimensions to be the most important, followed by the social and technical evaluation dimensions. The e-fuel followed by the e-biofuel are found to be the two top-ranked production pathways that utilise the electrochemical reduction process and its combination with anaerobic digestion. These findings, along with our recommendations, provide decision-makers with guidelines on ALCF production pathway selection and formulate effective policies for investment.
Lyons B, Bernardi A, Shah N, et al., 2023, Methane-to-X: an economic assessment of methane valorisation options to improve carbon circularity, Computer Aided Chemical Engineering, Pages: 2435-2440
Methane side streams are produced in many different chemical processes and are normally combusted to provide process heat or to generate electricity. However, this practice is becoming less and less attractive as the industry strives towards net-zero targets and increasing the circularity of chemicals. Methane could instead be recovered and used as a valuable feedstock to produce other platform chemicals, such as H2 or ethylene, which could be beneficial both for the economic performance and the carbon circularity of the system. In this work, seven different methane valorisation routes to produce additional chemicals are investigated. The considered routes include: i) five syngas-based routes combined with methanol synthesis and a methanol-to-olefins process; ii) plasma methane pyrolysis; and iii) oxidative coupling of methane. The results suggest that oxidative coupling of methane is the most profitable, with methane pyrolysis, tri-reforming and autothermal reforming also being more profitable in the base case. All routes have lower scope 1 and 2 emissions than the base case, however, dry-reforming and bi-reforming have the lowest emissions thanks to credited CO2 feed streams.
Sarkis M, Fung J, Lee MH, et al., 2023, Integrating environmental sustainability in next-generation biopharmaceutical supply chains, Computer Aided Chemical Engineering, Pages: 3405-3410
Maximizing product availability to the public and minimizing costs are primary objectives in the biopharmaceutical sector. Nevertheless, awareness of the environmental sustainability of supply chain operations is becoming increasingly relevant in recent years. To assist decision-makers in balancing financial and environmental sustainability we present an optimization framework which determines candidate supply chain structures network designs and operational plans. Supply chain structures are assessed with respect to total cost and environmental score, with the latter integrating environmental impacts related to climate change, water usage and energy consumption. A Pareto set of candidate solutions is found which provides insights in complex trade-offs between impact categories and cost: centralized manufacturing is selected to lower unit production cost and better use water resources, whilst decentralized manufacturing improves energy usage. Emissions from CO2 are lowered through cost minimization.
Triantafyllou N, Papaiakovou S, Bernardi A, et al., 2023, Machine learning-based decomposition for complex supply chains, Computer Aided Chemical Engineering, Pages: 1655-1660
Personalised medicine products represent a novel category of therapeutics often characterised by bespoke manufacturing lines and dedicated distribution nodes. An example of such products is Chimeric Antigen Receptor (CAR) T-cells, whose manufacturing poses challenges to volumetric scale-up, leading to increased production and supply chain costs. From a modelling perspective, such networks lead to complex large-scale supply chain models that grow exponentially as the demand increases and more therapies are tracked simultaneously throughout the supply chain. In this work, we present a hybrid model that utilizes the potential of machine learning for strategic planning by forecasting optimal supply chain structures and Mixed Integer Linear Programming (MILP) for detailed scheduling. The proposed model is robust to uncertain demand patterns and can reduce the number of linear constraints and binary variables in the original MILP by more than 64.7%.
Bernardi A, Casan DB, Symes A, et al., 2023, Enviro-economic assessment of sustainable aviation fuel production from direct CO<inf>2</inf> hydrogenation, Computer Aided Chemical Engineering, Pages: 2345-2350
The aviation industry is responsible for 2% of the total GHG emissions and 10% of the fuel consumption worldwide and sustainable aviation fuel (SAF) is considered a key step towards achieving net-zero aviation. In this work, we carry out an enviro-economic comparison of a one-step Fischer-Tropsch process (1sFT), based on a novel Mn-Fe-K catalyst, whereby CO2 and H2 are directly converted to liquid hydrocarbons, with a two-steps FT process (2sFT), in which a reverse water gas shift reactor is used to produce syngas, followed by a conventional FT process. Our analysis considers 1 MJ of liquid fuel as functional unit and the following key performance indicators: levelized cost of production, global warming potential, and monetized end-point environmental impacts. Our results suggest that the fuel blend from 1sFT has a minimum selling price 20% lower than the fuel blend from 2sFT, due to a lower capital cost and a higher selectivity towards liquid hydrocarbons. 1sFT is also found to be superior to 2sFT from an environmental point of view, with 30% lower GWP and 70% lower externalities cost.
Triantafyllou N, Bernardi A, Lakelin M, et al., 2022, A digital platform for the design of patient-centric supply chains, Scientific Reports, Vol: 12, ISSN: 2045-2322
Chimeric Antigen Receptor (CAR) T cell therapies have received increasing attention, showing promising results in the treatment of acute lymphoblastic leukaemia and aggressive B cell lymphoma. Unlike typical cancer treatments, autologous CAR T cell therapies are patient-specific; this makes them a unique therapeutic to manufacture and distribute. In this work, we focus on the development of a computer modelling tool to assist the design and assessment of supply chain structures that can reliably and cost-efficiently deliver autologous CAR T cell therapies. We focus on four demand scales (200, 500, 1000 and 2000 patients annually) and we assess the tool’s capabilities with respect to the design of responsive supply chain candidate solutions while minimising cost.
Sunny N, Bernardi A, Danaci D, et al., 2022, A pathway towards net-zero emissions in oil refineries, Frontiers in Chemical Engineering, Vol: 4, ISSN: 2673-2718
Rapid industrialization and urbanization have increased the demand for both energy and mobility services across the globe, with accompanying increases in greenhouse gas emissions. This short paper analyzes strategic measures for the abatement of CO2 emissions from oil refinery operations. A case study involving a large conversion refinery shows that the use of post-combustion carbon capture and storage (CCS) may only be practical for large combined emission point sources, leaving about 30% of site-wide emissions unaddressed. A combination of post-combustion CCS with a CO2 capture rate well above 90% and other mitigation measures such as fuel substitution and emission offsets is needed to transition towards carbon-neutral refinery operations. All of these technologies must be configured to minimize environmental burden shifting and scope 2 emissions, whilst doing so cost-effectively to improve energy access and affordability. In the long run, scope 3 emissions from the combustion of refinery products and flaring must also be addressed. The use of synthetic fuels and alternative feedstocks such as liquefied plastic waste, instead of crude oil, could present a growth opportunity in a circular carbon economy.
Triantafyllou N, Bernardi A, Lakelin M, et al., 2022, A bi-level decomposition approach for CAR-T cell therapies supply chain optimisation, Computer Aided Chemical Engineering, Pages: 2197-2202
Autologous cell therapies are based on bespoke, patient-specific manufacturing lines and distribution channels. They present a novel category of therapies with unique features that impose scale out approaches. Chimeric Antigen Receptor (CAR) T cells are an example of such products, the manufacturing of which is based on the patient's own cells. This automatically: (a) creates dependencies between the patient and the supply chain schedules and (b) increases the associated costs, as manufacturing lines and distribution nodes are exclusive to the production and delivery of a single therapy. The lack of scale up opportunities and the tight return times required, dictate the design of agile and responsive distribution networks that are eco-efficient. From a modelling perspective, such networks are described by a large number of variables and equations, rendering the problem intractable. In this work, we present a bi-level decomposition algorithm as means to reduce the computational complexity of the original Mixed Integer Linear Programming (MILP) model. Optimal solutions for the structure and operation of the supply chain network are obtained for demands of up to 5000 therapies per year, in which case the original model contains 68 million constraints and 16 million discrete variables.
Bernardi A, Bello F, Valente A, et al., 2022, Enviro-economic assessment of DME synthesis using carbon capture and hydrogen from methane pyrolysis, Computer Aided Chemical Engineering, Pages: 1003-1008
The catalytic conversion of captured CO2 and H2 into fuels is recognised as an interesting option to decarbonise the transport sector in the short-midterm future. DME has been identified as an ideal diesel-substitute for heavy-duty vehicles due to its high cetane number and excellent combustion properties, but to be competitive with diesel a low-cost and low-carbon H2 production route is a key enabler. Recent developments indicate that methane pyrolysis has the potential to produce H2 at a similar cost compared to steam methane reforming, the main H2 production route nowadays, yet with no direct CO2 emissions. This paper presents an enviro-economic assessment of 12 life-cycle pathways for DME production. Our results show that DME produced using H2 from methane pyrolysis could be competitive with diesel, both economically and environmentally, but is highly dependent upon the utilisation of the carbon by-product.
Triantafyllou N, Bernardi A, Lakelin M, et al., 2022, Fresh vs frozen: assessing the impact of cryopreservation in personalised medicine, Computer Aided Chemical Engineering, Pages: 955-960
Chimeric Antigen Receptor (CAR) T cell therapy is a type of patient-specific cell immunotherapy demonstrating promising results in the treatment of aggressive haematological malignancies. Autologous CAR T cell therapies are based on bespoke manufacturing lines and distribution nodes that are exclusive to the production and delivery of a single therapy. Given their patient-specific nature, they follow a 1:1 business model that challenges volumetric scale up, leading to increased manufacturing and distribution costs. Manufacturers aim to guarantee the in-time delivery and identify ways to reduce the production cost with the ultimate objective of releasing these innovative therapies to a bigger portion of the population. In this work, we investigate upstream storage to the supply chain network as means to introduce greater flexibility in the modus operandi. We formulate and assess different supply chain networks via a Mixed Integer Linear Programming model.
Bernardi A, Sarkis M, Triantafyllou N, et al., 2022, Assessment of intermediate storage and distribution nodes in personalised medicine, Computers & Chemical Engineering, Vol: 157, Pages: 107582-107582, ISSN: 0098-1354
Chimeric Antigen Receptor (CAR) T cell therapies are a type of patient-specific cell immunotherapy demonstrating promising results in the treatment of aggressive blood cancer types. CAR T cells follow a 1:1 business model, translating into manufacturing lines and distribution nodes being exclusive to the production of a single therapy, hindering volumetric scale up. In this work, we address manufacturing capacity bottlenecks via a Mixed Integer Linear Programming (MILP) model. The proposed formulation focuses on the design of candidate supply chain network configurations under different demand scenarios. We investigate the effect of an intermediate storage upstream of the network to: (a) debottleneck manufacturing lines and (b) increase facility utilisation. In this setting, we assess cost-effectiveness and flexibility of the supply chain and we evaluate network performance with respect to: (a) average production cost and (b) average response treatment time. The trade-off between cost-efficiency and responsiveness is examined and discussed.
Sarkis M, Bernardi A, Shah N, et al., 2021, Decision support tools for next-generation vaccines and advanced therapy medicinal products: present and future, Current Opinion in Chemical Engineering, Vol: 32, Pages: 100689-100689, ISSN: 2211-3398
Advanced Therapy Medicinal Products (ATMPs) are a novel class of biological therapeutics that utilise ground-breaking clinical interventions to prevent and treat life-threatening diseases. At the same time, viral vector-based and RNA-based platforms introduce a new generation of vaccine manufacturing processes. Their clinical success has led to an unprecedented rise in the demand that, for ATMPs, leads to a predicted market size of USD 9.6 billion by 2026. This paper discusses how mathematical models can serve as tools to assist decision-making in development, manufacturing and distribution of these new product classes. Recent contributions in the space of process, techno-economic and supply chain modelling are highlighted. Lastly, we present and discuss how Process Systems Engineering can be further advanced to support commercialisation of advanced therapeutics and vaccines.
Sarkis M, Bernardi A, Shah N, et al., 2021, Emerging challenges and opportunities in pharmaceutical manufacturing and distribution, Processes, Vol: 9, Pages: 1-16, ISSN: 2227-9717
The rise of personalised and highly complex drug product profiles necessitates significant advancements in pharmaceutical manufacturing and distribution. Efforts to develop more agile, responsive, and reproducible manufacturing processes are being combined with the application of digital tools for seamless communication between process units, plants, and distribution nodes. In this paper, we discuss how novel therapeutics of high-specificity and sensitive nature are reshaping well-established paradigms in the pharmaceutical industry. We present an overview of recent research directions in pharmaceutical manufacturing and supply chain design and operations. We discuss topical challenges and opportunities related to small molecules and biologics, dividing the latter into patient- and non-specific. Lastly, we present the role of process systems engineering in generating decision-making tools to assist manufacturing and distribution strategies in the pharmaceutical sector and ultimately embrace the benefits of digitalised operations.
Bernardi A, Papathanasiou M, Lakelin MW, et al., 2021, Assessment of intermediate storage and distribution nodes in personalised medicine, Computer Aided Chemical Engineering, Pages: 1997-2002
Chimeric Antigen Receptor (CAR)-T cell therapies are a type of patient-specific cell immunotherapy demonstrating promising results in the treatment of aggressive blood cancer types. CAR-T cells follow a 1:1 business model, translating into manufacturing lines and distribution nodes being exclusive to the production of a single therapy, hindering volumetric scale up. In this work, we address manufacturing capacity bottlenecks via a Mixed Integer Linear Programming (MILP) model. The proposed formulation focuses on the design of candidate supply chain network configurations under different demand scenarios. We investigate the effect of an intermediate storage option upstream of the network as means of: (a) debottlenecking manufacturing lines and (b) increasing facility utilisation. In this setting, we assess cost-effectiveness and flexibility of a decentralised supply chain and we evaluate network performance with respect to two key performance indicators (KPIs): (a) average production cost and (b) average response treatment time. The trade-off between cost-efficiency and responsiveness is examined and discussed.
Al-Qahtani A, Gonzalez-Garay A, Bernardi A, et al., 2020, Electricity grid decarbonisation or green methanol fuel? A life-cycle modelling and analysis of today's transportation-power nexus, APPLIED ENERGY, Vol: 265, ISSN: 0306-2619
Bernardi A, Chen Y, Chadwick D, et al., 2020, Direct DME Synthesis from Syngas: a Technoeconomic Model-based Investigation, Editors: Pierucci, Manenti, Bozzano, Manca, Publisher: ELSEVIER SCIENCE BV, Pages: 655-660
Perin G, Bellan A, Bernardi A, et al., 2019, The potential of quantitative models to improve microalgae photosynthetic efficiency, Physiologia Plantarum, Vol: 166, Pages: 380-391, ISSN: 1399-3054
The massive increase in carbon dioxide concentration in the atmosphere driven by human activities is causing huge negative consequences and new sustainable sources of energy, food and materials are highly needed. Algae are unicellular photosynthetic microorganisms that can provide a highly strategic contribution to this challenge as alternative source of biomass to complement crops cultivation. Algae industrial cultures are commonly limited by light availability, and biomass accumulation is strongly dependent on their photon-to-biomass conversion efficiency. Investigation of algae photosynthetic metabolism is thus strategic for the generation of more efficient strains with higher productivity.
Bernardi A, Gomoescu L, Wang J, et al., 2019, Kinetic Model Discrimination for Methanol and DME Synthesis using Bayesian Estimation, 12th International-Federation-of-Automatic-Control (IFAC) Symposium on Dynamics and Control of Process Systems including Biosystems (DYCOPS), Publisher: ELSEVIER SCIENCE BV, Pages: 335-340, ISSN: 2405-8963
Bernardi A, Graciano JEA, Chachuat B, 2019, Production of chemicals from syngas: an enviro-economic model-based investigation, Editors: Kiss, Zondervan, Lakerveld, Ozkan, Publisher: ELSEVIER SCIENCE BV, Pages: 367-372, ISBN: 978-0-12-819939-8
De-Luca R, Bernardi A, Meneghesso A, et al., 2018, Modelling the photosynthetic electron transport chain in Nannochloropsis gaditana via exploitation of absorbance data, Algal Research, Vol: 33, Pages: 430-439, ISSN: 2211-9264
© 2018 Elsevier B.V. The development of mathematical models describing the photosynthetic apparatus of microalgae is paramount to gain deeper knowledge of the involved biological process and enable optimisation of cultivation conditions. This paper presents a dynamic model of the entire photosynthetic apparatus including the photosystems I (PSI) and II (PSII), the electron carriers between the two photosystems (plastoquinone, cytochrome b6f and cytochrome c6) and the final electron acceptor complex, the ferredoxin. In vivo measurements of PSI oxidation dynamics at different light intensities for the microalga Nannochloropsis gaditana have been exploited to develop and calibrate the model. The model has been experimentally identified and proved to be capable of accurate predictions of both linear and cyclic electron flows dependence on light intensity.
Perin G, Bernardi A, Bellan A, et al., 2017, A Mathematical model to guide Genetic Engineering of Photosynthetic Metabolism, Metabolic Engineering, ISSN: 1096-7176
The optimization of algae biomass productivity in industrial cultivation systems requires genetic improvement of wild type strains isolated from nature. One of the main factors affecting algae productivity is their efficiency in converting light into chemical energy and this has been a major target of recent genetic efforts. However, photosynthetic productivity in algae cultures depends on many environmental parameters, making the identification of advantageous genotypes complex and the achievement of concrete improvements slow.In this work, we developed a mathematical model to describe the key factors influencing algae photosynthetic productivity in a photobioreactor, using experimental measurements for the WT strain of Nannochloropsis gaditana. The model was then exploited to predict the effect of potential genetic modifications on algae performances in an industrial context, showing the ability to predict the productivity of mutants with specific photosynthetic phenotypes. These results show that a quantitative model can be exploited to identify the genetic modifications with the highest impact on productivity taking into full account the complex influence of environmental conditions, efficiently guiding engineering efforts.
Nikolaou A, Bernardi A, Meneghesso A, et al., 2016, High-Fidelity Modelling Methodology of Light-Limited Photosynthetic Production in Microalgae, PLOS One, Vol: 11, ISSN: 1932-6203
Reliable quantitative description of light-limited growth in microalgae is key to improving the design and operation of industrial production systems. This article shows how the capability to predict photosynthetic processes can benefit from a synergy between mathematical modelling and lab-scale experiments using systematic design of experiment techniques. A model of chlorophyll fluorescence developed by the authors [Nikolaou et al., J Biotechnol 194:91–99, 2015] is used as starting point, whereby the representation of non-photochemical-quenching (NPQ) process is refined for biological consistency. This model spans multiple time scales ranging from milliseconds to hours, thus calling for a combination of various experimental techniques in order to arrive at a sufficiently rich data set and determine statistically meaningful estimates for the model parameters. The methodology is demonstrated for the microalga Nannochloropsis gaditana by combining pulse amplitude modulation (PAM) fluorescence, photosynthesis rate and antenna size measurements. The results show that the calibrated model is capable of accurate quantitative predictions under a wide range of transient light conditions. Moreover, this work provides an experimental validation of the link between fluorescence and photosynthesis-irradiance (PI) curves which had been theoricized.
Bernardi A, Meneghesso A, Morosinotto T, et al., 2016, A model-based investigation of genetically modified microalgae strains, Computer Aided Chemical Engineering, Pages: 607-612, ISBN: 9780444634283
Genetic modification of microalgal strains can be an effective tool to close the gap between the theoretical and realised quantum efficiency in industrial scale photobioreactors. In this paper, we want to propose a model-based approach to compare different mutants using fast and accurate fluorescence measurements along with some photosynthesis rate measurements, in order to develop a methodology to rapidly assess the performances of different mutants in a way limiting long experimental campaign. A model developed by the authors, able to reproduce fluorescence fluxes and photosynthesis rate measurements for the wild type, will be used to: (i) predict the behaviour of an ideal NPQ-less mutant based on the wild type data and (ii) predict the photosynthesis rate of a real mutant, in which the NPQ mechanisms have been inhibited, using fluorescence data to calibrate the NPQ-related parameters. Furthermore, the performances of the mutants will be tested considering the light profiles of a summer and a winter month of a Mediterranean country.
Nikolaou A, Bernardi A, Meneghesso A, et al., 2015, A model of chlorophyll fluorescence in microalgae integrating photoproduction, photoinhibition and photoregulation, Journal of Biotechnology, Vol: 194, Pages: 91-99, ISSN: 0168-1656
This paper presents a mathematical model capable of quantitative prediction of the state of the photosynthetic apparatus of microalgae in terms of their open, closed and damaged reaction centers under variable light conditions. This model combines the processes of photoproduction and photoinhibition in the Han model with a novel mathematical representation of photoprotective mechanisms, including qE-quenching and qI-quenching. For calibration and validation purposes, the model can be used to simulate fluorescence fluxes, such as those measured in PAM fluorometry, as well as classical fluorescence indexes. A calibration is carried out for the microalga Nannochloropsis gaditana, whereby 9 out of the 13 model parameters are estimated with good statistical significance using the realized, minimal and maximal fluorescence fluxes measured from a typical PAM protocol. The model is further validated by considering a more challenging PAM protocol alternating periods of intense light and dark, showing a good ability to provide quantitative predictions of the fluorescence fluxes even though it was calibrated for a different and somewhat simpler PAM protocol. A promising application of the model is for the prediction of PI-response curves based on PAM fluorometry, together with the long-term prospect of combining it with hydrodynamic and light attenuation models for high-fidelity simulation and optimization of full-scale microalgae production systems.
Bernardi A, Nikolaou A, Meneghesso A, et al., 2015, A Framework for the Dynamic Modelling of PI Curves in Microalgae, 12TH INTERNATIONAL SYMPOSIUM ON PROCESS SYSTEMS ENGINEERING AND 25TH EUROPEAN SYMPOSIUM ON COMPUTER AIDED PROCESS ENGINEERING, PT C, Vol: 37, Pages: 2483-2488, ISSN: 1570-7946
Bernardi A, Nikolaou A, Meneghesso A, et al., 2015, Using Fluorescence Measurements to Model Key Phenomena in Microalgae Photosynthetic Mechanisms, ICHEAP12: 12TH INTERNATIONAL CONFERENCE ON CHEMICAL & PROCESS ENGINEERING, Vol: 43, Pages: 217-222, ISSN: 2283-9216
Bernardi A, Perin G, Sforza E, et al., 2014, An identifiable state model to describe light intensity influence on microalgae growth, Industrial && Engineering Chemistry Research, Vol: 53, Pages: 6738-6749, ISSN: 0888-5885
Despite the high potential as feedstock for the production of fuels and chemicals, the industrial cultivation of microalgae still exhibits many issues. Yield in microalgae cultivation systems is limited by the solar energy that can be harvested. The availability of reliable models representing key phenomena affecting algae growth may help designing and optimizing effective production systems at an industrial level. In this work the complex influence of different light regimes on seawater alga Nannochloropsis salina growth is represented by first principles models. Experimental data such as in vivo fluorescence measurements are employed to develop the model. The proposed model allows description of all growth curves and fluorescence data in a reliable way. The model structure is assessed and modified in order to guarantee the model identifiability and the estimation of its parametric set in a robust and reliable way.
Nikolaou A, Bernardi A, Bezzo F, et al., 2014, Dynamic Model of Photoproduction, Photoregulation and Photoinhibition in Microalgae using Chlorophyll Fluorescence., IFAC WC, Publisher: Elsevier
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.