29 results found
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.
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.
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, 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.
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
- Author Web Link
- Citations: 15
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
- Author Web Link
- Citations: 5
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
- Author Web Link
- Citations: 6
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
Bernardi A, Perin G, Galvanin F, et al., 2013, Modeling the Effect of Light Intensity in Microalgae Growth, Publisher: 2013 AIChE Annual Meeting
Bernardi A, Giarola S, Bezzo F, 2013, Spatially Explicit Multiobjective Optimization for the Strategic Design of First and Second Generation Biorefineries Including Carbon and Water Footprints, INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, Vol: 52, Pages: 7170-7180, ISSN: 0888-5885
- Author Web Link
- Citations: 42
Bernardi A, Perin G, Galvanin F, et al., 2013, Modeling the effect of light intensity in microalgae growth, Pages: 349-350
- Citations: 1
Gutierrez RAO, Penazzi S, Bernardi A, et al., 2013, A spatially-explicit approach to the design of ethanol supply chains considering multiple technologies and carbon trading effects, 23 EUROPEAN SYMPOSIUM ON COMPUTER AIDED PROCESS ENGINEERING, Vol: 32, Pages: 643-648, ISSN: 1570-7946
- Author Web Link
- Citations: 1
Bernardi A, Giarola S, Bezzo F, 2012, Optimizing the economics and the carbon and water footprints of bioethanol supply chains, BIOFUELS BIOPRODUCTS & BIOREFINING-BIOFPR, Vol: 6, Pages: 656-672, ISSN: 1932-104X
- Author Web Link
- Citations: 37
Bernardi A, Giarola S, Bezzo F, 2012, A framework for water footprint optimisation in the bioethanol supply chain, 11TH INTERNATIONAL SYMPOSIUM ON PROCESS SYSTEMS ENGINEERING, PTS A AND B, Vol: 31, Pages: 1372-1376, ISSN: 1570-7946
- Author Web Link
- Citations: 2
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