Advances and Future Perspectives

Agharafeie , R., Ramos, J. R. C., Mendes, J. M., & Oliveira, R. M. F. (2023). From Shallow to Deep Bioprocess Hybrid Modeling: Advances and Future Perspectives. Fermentation, 9(10), 1-22. [922]. https://doi.org/10.20944/preprints202310.0107.v1, https://doi.org/10.3390/fermentation9100922--- This work was supported by the Associate Laboratory for Green Chemistry - LAQV which is financed by national funds from FCT/MCTES (UIDB/50006/2020 and UIDP/50006/2020). This work received funding from the European Union’s Horizon 2020 research and innovation program under the grant agreement no. 101099487- BioLaMer-HORIZON-EIC-2022-PATHFINDEROPEN-01 (BioLaMer)Deep learning is emerging in many industrial sectors in hand with big data analytics to streamline production. In the biomanufacturing sector, big data infrastructure is lagging comparatively to other industries. A promising approach is to combine Deep Neural Networks (DNN) with prior knowledge in Hybrid Neural Network (HNN) workflows that are less dependent on the quality and quantity of data. This paper reviews published articles over the past 30 years on the topic of HNN applications to bioprocesses. It revealed that HNNs were applied to various bioprocesses, including microbial cultures, animal cells cultures, mixed microbial cultures, and enzyme biocatalysis. HNNs were mainly applied for process analysis, process monitoring, development of software sensors, open- and closed-loop control, batch-to-batch control, model predictive control, intensified design of experiments, quality-by-design, and recently for the development of digital twins. Most previous HNN studies combined shallow Feedforward Neural Networks (FFNNs) with physical laws, such as macroscopic material balance equations, following the semiparametric design principle. Only recently, deep HNNs based on deep FFNNs, Convolution Neural Networks (CNN), Long Short-Term Memory (LSTM) networks and Physics Informed Neural Networks (PINNs) have been reported. The biopharma sector is currently a major driver but applications to biologics quality attributes, new modalities, and downstream processing are significant research gaps.publishersversionpublishe

Novel strategies for process control based on hybrid semi-parametric mathematical systems

Tese de doutoramento. Engenharia Química. Universidade do Porto. Faculdade de Engenharia. 201

Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools

This research topic aims at enterprise-wide modeling and optimization (EWMO) through the development and application of integrated modeling, simulation and optimization methodologies, and computer-aided tools for reliable and sustainable improvement opportunities within the entire manufacturing network (raw materials, production plants, distribution, retailers, and customers) and its components. This integrated approach incorporates information from the local primary control and supervisory modules into the scheduling/planning formulation. That makes it possible to dynamically react to incidents that occur in the network components at the appropriate decision-making level, requiring fewer resources, emitting less waste, and allowing for better responsiveness in changing market requirements and operational variations, reducing cost, waste, energy consumption and environmental impact, and increasing the benefits. More recently, the exploitation of new technology integration, such as through semantic models in formal knowledge models, allows for the capture and utilization of domain knowledge, human knowledge, and expert knowledge toward comprehensive intelligent management. Otherwise, the development of advanced technologies and tools, such as cyber-physical systems, the Internet of Things, the Industrial Internet of Things, Artificial Intelligence, Big Data, Cloud Computing, Blockchain, etc., have captured the attention of manufacturing enterprises toward intelligent manufacturing systems

Additive Manufacturing of Bio and Synthetic Polymers

Additive manufacturing technology offers the ability to produce personalized products with lower development costs, shorter lead times, less energy consumed during manufacturing and less material waste. It can be used to manufacture complex parts and enables manufacturers to reduce their inventory, make products on-demand, create smaller and localized manufacturing environments, and even reduce supply chains. Additive manufacturing (AM), also known as fabricating three-dimensional (3D) and four-dimensional (4D) components, refers to processes that allow for the direct fabrication of physical products from computer-aided design (CAD) models through the repetitious deposition of material layers. Compared with traditional manufacturing processes, AM allows the production of customized parts from bio- and synthetic polymers without the need for molds or machining typical for conventional formative and subtractive fabrication.In this Special Issue, we aimed to capture the cutting-edge state-of-the-art research pertaining to advancing the additive manufacturing of polymeric materials. The topic themes include advanced polymeric material development, processing parameter optimization, characterization techniques, structure–property relationships, process modelling, etc., specifically for AM

Bioconversion of biodegradable municipal solid waste (BMSW) to glucose for bio-ethanol production.

Municipal solid waste (MSW), as an emerging biomass source, presents a unique opportunity for large-scale second-generation bioethanol production. Feedstock supply is reliable and in sufficient quantity, making it a promising biomass source but the conversion yield is currently too low to make it financially attractive. This work presented in this thesis provides a better understanding of bioconversion systems, in particular of pre-treatment and hydrolysis processes which contribute to more than 60% of ethanol selling price. This thesis also presents a technique of bioconversion which allows conversion of MSW to bioethanol to be carried out more efficiently than with existing techniques. This thesis starts with an assessment of the feasibility of using MSW to replace primary agricultural products as biomass sources. It presents an efficient MSW to ethanol bioconversion process which includes pre-treatment and enzymatic hydrolysis, and provides detailed quantitative information on the conditions that maximise the glucose yield to 80% after 24-h hydrolysis reaction. This thesis also presents the result of the characterisation of the complex substrate features of the selected MSW fractions which have lignin and cellulose crystallinity, and an evaluation of the effects of MSW-substrate features on the conversion process. Finally, it presents the first model of the effects of substrate features in cellulase-cellulose adsorption cellulase-cellulose adsorption is recognised as a crucial step that controls the enzymatic hydrolysis rate. This study shows that lignin, crystallinity, cellulose content and their interaction have an important influence on enzyme adsorption capacity. It is concluded that both lignin content and crystallinity play a greater role in cellulose-cellulase adsorption than cellulose content. Finally the presence of lignin has a greater effect than crystallinity on both the maximum enzyme adsorption capacity and steady-state enzyme adsorption, whereas crystallinity has a greater effect on the latter one

Process analytical technology in food biotechnology

Biotechnology is an area where precision and reproducibility are vital. This is due to the fact that products are often in form of food, pharmaceutical or cosmetic products and therefore very close to the human being. To avoid human error during the production or the evaluation of the quality of a product and to increase the optimal utilization of raw materials, a very high amount of automation is desired. Tools in the food and chemical industry that aim to reach this degree of higher automation are summarized in an initiative called Process Analytical Technology (PAT). Within the scope of the PAT, is to provide new measurement technologies for the purpose of closed loop control in biotechnological processes. These processes are the most demanding processes in regards of control issues due to their very often biological rate-determining component. Most important for an automation attempt is deep process knowledge, which can only be achieved via appropriate measurements. These measurements can either be carried out directly, measuring a crucial physical value, or if not accessible either due to the lack of technology or a complicated sample state, via a soft-sensor.Even after several years the ideal aim of the PAT initiative is not fully implemented in the industry and in many production processes. On the one hand a lot effort still needs to be put into the development of more general algorithms which are more easy to implement and especially more reliable. On the other hand, not all the available advances in this field are employed yet. The potential users seem to stick to approved methods and show certain reservations towards new technologies.Die Biotechnologie ist ein Wissenschaftsbereich, in dem hohe Genauigkeit und Wiederholbarkeit eine wichtige Rolle spielen. Dies ist der Tatsache geschuldet, dass die hergestellten Produkte sehr oft den Bereichen Nahrungsmitteln, Pharmazeutika oder Kosmetik angehöhren und daher besonders den Menschen beeinflussen. Um den menschlichen Fehler bei der Produktion zu vermeiden, die Qualität eines Produktes zu sichern und die optimale Verwertung der Rohmaterialen zu gewährleisten, wird ein besonders hohes Maß an Automation angestrebt. Die Werkzeuge, die in der Nahrungsmittel- und chemischen Industrie hierfür zum Einsatz kommen, werden in der Process Analytical Technology (PAT) Initiative zusammengefasst. Ziel der PAT ist die Entwicklung zuverlässiger neuer Methoden, um Prozesse zu beschreiben und eine automatische Regelungsstrategie zu realisieren. Biotechnologische Prozesse gehören hierbei zu den aufwändigsten Regelungsaufgaben, da in den meisten Fällen eine biologische Komponente der entscheidende Faktor ist. Entscheidend für eine erfolgreiche Regelungsstrategie ist ein hohes Maß an Prozessverständnis. Dieses kann entweder durch eine direkte Messung der entscheidenden physikalischen, chemischen oder biologischen Größen gewonnen werden oder durch einen SoftSensor. Zusammengefasst zeigt sich, dass das finale Ziel der PAT Initiative auch nach einigen Jahren des Propagierens weder komplett in der Industrie noch bei vielen Produktionsprozessen angekommen ist. Auf der einen Seite liegt dies mit Sicherheit an der Tatsache, dass noch viel Arbeit in die Generalisierung von Algorithmen gesteckt werden muss. Diese müsse einfacher zu implementieren und vor allem noch zuverlässiger in der Funktionsweise sein. Auf der anderen Seite wurden jedoch auch Algorithmen, Regelungsstrategien und eigne Ansätze für einen neuartigen Sensor sowie einen Soft-Sensors vorgestellt, die großes Potential zeigen. Nicht zuletzt müssen die möglichen Anwender neue Strategien einsetzen und Vorbehalte gegenüber unbekannten Technologien ablegen

Process regime classification and modelling of a sequencing batch reactor for producing polyhydroxybutyrate with mixed culture using neural networks

PhD ThesisIn recent years, there has been a growing incentive towards production and application of environmentally benign materials with properties similar to those obtained from irreplaceable resources or exhibiting harmful effects on the environment. In this respect, bioplastics have gained attention in quest of materials that can be used in place of conventional petro-chemical plastics. Biocompatibility, biodegradability and compostability of bioplastics are among the most favourable characteristics of the materials mostly derived from biological systems. Polyhydroxybutyrate (PHB) is a fully biodegradable bioplastic with similar physical properties to polyethylene and promising applications in various commercial fields including automation, aviation, medication, nutrition, fuel, packaging and many more. PHB production with Mixed Microbial Cultures (MMC) has recently gained attention as a cost effective production strategy by using bacteria that adapt with complex substrates presented in inexpensive waste materials. The initial research motivation was to enhance PHB production operation by means of the solutions obtained from sophisticated mathematical algorithms used for process optimisation. For this aim, a computer-based program simulating PHB batch process with MMC which was successfully validated with experimental data was available. Since mechanistic models of the simulation program could not be applied in optimisation algorithms, accurate empirical models were required. In the quest for reliable and accurate empirical models that can predict product concentration at the final stage of a batch operation, a methodology was developed in this study for classification of the batch operational regions based on the PHB critical process attributes. In the core of this research work, an innovative systematic methodology improves process understanding towards advanced process monitoring and control. This method enables operational scrutiny for generation of process knowledge regarding PHB process using MMC. The qualitative info-illustrations produced in the course of the classification method provide a sound platform for generation of considerably more accurate (quantitative) empirical models. These empirical models will be used in process optimisation studies. Abstract III In this research, PHB production occurs in a process type known as “feast and famine” or as “aerobic dynamic feeding” which is a well-known strategy applied for bacterial production with MMC. The “feast and famine” operations take place in Sequential Batch Reactors (SBR) in order to assure occurrence of the “feast” and the “famine” phases intermittently in each operational cycle. While PHB formation occurs during the “feast” phase, a “famine” phase should be followed to cause a cell physiological adaptation to maintain PHB production capability of bacteria. Establishment of the analytical methodology developed in direction of process empirical modelling realisation enables prediction of “feast” and “famine” phase occurrences based on the batch initial state documented for the first time in this work. This mathematical equation (“Phase Differentiating Equation”) plays a significant role in development of a novel SBR recipe for production of PHB with MMC. Execution of the recipe by the PHB process simulation program demonstrates high reliability of the proposed recipe. Application of the “Phase Differentiating Equation” in the SBR recipe assures favourable occurrence of the “feast” and “famine” phases in the majority of operational cycles. Reduction of operational failure rate reduces PHB production cost to improve its market position. The SBR recipe structure consists of six-stage cycles including (1) “feast” phase preparation stage, (2) “feast” phase operation, (3) operational quiescence, (4) product exploitation, (5) “famine” phase preparation stage and (6) “famine” phase operation. Operational reliability is investigated along with load disturbance rejection embedded in the SBR recipe. At the end, Sequential Quadratic Programming (SQP) is applied successfully as an optimisation algorithm to maximise PHB production under operational constrains