Deep learning based surrogate modeling and optimization for Microalgal biofuel production and photobioreactor design

Identifying optimal photobioreactor configurations and process operating conditions is critical to industrialize microalgae-derived biorenewables. Traditionally, this was addressed by testing numerous design scenarios from integrated physical models coupling computational fluid dynamics and kinetic modelling. However, this approach presents computational intractability and numerical instabilities when simulating large-scale systems, causing time-intensive computing efforts and infeasibility in mathematical optimization. Therefore, we propose an innovative data-driven surrogate modelling framework which considerably reduces computing time from months to days by exploiting state-of-the-art deep learning technology. The framework built upon a few simulated results from the physical model to learn the sophisticated hydrodynamic and biochemical kinetic mechanisms; then adopts a hybrid stochastic optimization algorithm to explore untested processes and find optimal solutions. Through verification, this framework was demonstrated to have comparable accuracy to the physical model. Moreover, multi-objective optimization was incorporated to generate a Pareto-frontier for decision-making, advancing its applications in complex biosystems modelling and optimization

Integrating bioprocesses into industrial complexes for sustainable development

The objective of this research is to propose, develop and demonstrate a methodology for the optimal integration of bioprocesses in an existing chemical production complex. Chemical complex optimization is determining the optimal configuration of chemical plants in a superstructure of possible plants based on economic, environmental and sustainable criteria objective function (triple bottomline) and solves a mixed integer non linear programming problem. This research demonstrated the transition of production of chemicals from non-renewable to renewable feedstock. A conceptual design of biochemical processes was converted to five industrial scale designs in Aspen HYSYS® process simulator. Fourteen input-output block models were created from the designs based on the mass and energy relations. A superstructure of plants was formed by integrating the bioprocess models into a base case of existing plants in the lower Mississippi River corridor. Carbon dioxide produced from the integrated complex was used for algae oil and new chemicals production. The superstructure had 978 equality constraints, 91 inequality constraints, 969 continuous variables and 25 binary variables. The optimal solution gave a triple bottomline profit of $1,650 million per year from the base case solution of$854 million per year (93% increase). Raw material costs in the optimal solution decreased by 31% due to the exclusion of the costly ethylbenzene process. The utility costs for the complex increased to $46 million per year from$12 million per year. The sustainable costs to the society decreased to $10 million per year from$18 million per year (44% decrease). The bioprocesses increased the pure carbon dioxide sources to 1.07 million metric tons per year from 0.75 million metric tons per year for the base case (43% increase). The pure carbon dioxide vented to the atmosphere was reduced to zero in the optimal structure from 0.61 million metric tons per year (100% decrease) by consumption in the complex. The methodology can be used by decision makers to evaluate energy efficient and environmentally acceptable plants and have new products from greenhouse gases. Based on these results, the methodology could be applied to other chemical complexes in the world for reduced emissions and energy savings

Modelling and simulation based assessment in sustainable bioprocess development

Modelling and simulation enhance our insight and understanding of chemical processes and aid in identifying bottlenecks and potential improvements. A simplified simulation package, providing a reasonable estimate of material and energy usage and process emissions is often valuable in very early stages of process development, when temporal and financial limitations do not allow for more detailed estimates. Environmental burdens are an increasing concern in industrial processes and various methodologies and tools have been developed for gathering and analysis of process information to enhance understanding of the process system and inform decision makers. The systems nature of these approaches is aimed at mitigation of environmental burdens through improved technologies, sustainable resource consumption and screening of process alternatives. Ideally, the process design team should bring together these tools in early stages of development when design flexibility is greatest. In the present study, such a simplified approach to bioprocess design is demonstrated using a case study for the large-scale production of citric acid

Bioprocessing 4.0: a pragmatic review and future perspectives

In the dynamic landscape of industrial evolution, Industry 4.0 (I4.0) presents opportunities to revolutionise products, processes, and production. It is now clear that enabling technologies of this paradigm, such as the industrial internet of things (IIoT), artificial intelligence (AI), and Digital Twins (DTs), have reached an adequate level of technical maturity in the decade that followed the inception of I4.0. These technologies enable more agile, modular, and efficient operations, which are desirable business outcomes for particularly biomanufacturing companies seeking to deliver on a heterogeneous pipeline of treatments and drug product portfolios. Despite the widespread interest in the field, the level of adoption of I4.0 technologies in the biomanufacturing industry is scarce, often reserved to the big pharmaceutical manufacturers that can invest the capital in experimenting with new operating models, even though by now AI and IIoT have been democratised. This shift in approach to digitalisation is hampered by the lack of common standards and know-how describing ways I4.0 technologies should come together. As such, for the first time, this work provides a pragmatic review of the field, key patterns, trends, and potential standard operating models for smart biopharmaceutical manufacturing. This analysis aims to describe how the Quality by Design framework can evolve to become more profitable under I4.0, the recent advancements in digital twin development and how the expansion of the Process Analytical Technology (PAT) toolbox could lead to smart manufacturing. Ultimately, we aim to summarise guiding principles for executing a digital transformation strategy and outline operating models to encourage future adoption of I4.0 technologies in the biopharmaceutical industry

Continuous Biochemical Processing: Investigating Novel Strategies to Produce Sustainable Fuels and Pharmaceuticals

Biochemical processing methods have been targeted as one of the potential renewable strategies for producing commodities currently dominated by the petrochemical industry. To design biochemical systems with the ability to compete with petrochemical facilities, inroads are needed to transition from traditional batch methods to continuous methods. Recent advancements in the areas of process systems and biochemical engineering have provided the tools necessary to study and design these continuous biochemical systems to maximize productivity and substrate utilization while reducing capital and operating costs. The first goal of this thesis is to propose a novel strategy for the continuous biochemical production of pharmaceuticals. The structural complexity of most pharmaceutical compounds makes chemical synthesis a difficult option, facilitating the need for their biological production. To this end, a continuous, multi-feed bioreactor system composed of multiple independently controlled feeds for substrate(s) and media is proposed to freely manipulate the bioreactor dilution rate and substrate concentrations. The optimal feed flow rates are determined through the solution to an optimal control problem where the kinetic models describing the time-variant system states are used as constraints. This new bioreactor paradigm is exemplified through the batch and continuous cultivation of β-carotene, a representative product of the mevalonate pathway, using Saccharomyces cerevisiae strain mutant SM14. The second goal of this thesis is to design continuous, biochemical processes capable of economically producing alternative liquid fuels. The large-scale, continuous production of ethanol via consolidated bioprocessing (CBP) is examined. Optimal process topologies for the CBP technology selected from a superstructure considering multiple biomass feeds, chosen from those available across the United States, and multiple prospective pretreatment technologies. Similarly, the production of butanol via acetone-butanol-ethanol (ABE) fermentation is explored using process intensification to improve process productivity and profitability. To overcome the inhibitory nature of the butanol product, the multi-feed bioreactor paradigm developed for pharmaceutical production is utilized with in situ gas stripping to simultaneously provide dilution effects and selectively remove the volatile ABE components. Optimal control and process synthesis techniques are utilized to determine the benefits of gas stripping and design a butanol production process guaranteed to be profitable

Optimization of Bioprocesses for multiple objectives

Ph.DDOCTOR OF PHILOSOPH

Sustainable Design of Wastewater Treatment Systems: Evaluations of Operational Flexibility and Phototrophs for Resource Recovery.

The overarching goal of this dissertation is to advance the sustainability of wastewater systems. Although concepts surrounding sustainable wastewater infrastructure have advanced in recent years, a defined methodology to develop designs and elucidate trade-offs across dimensions of sustainability (social, economic, environmental, functional), space (local, regional, global), and time (present, future) does not exist. In particular, social barriers have not been sufficiently addressed and there is a lack of integration in quantitative assessments of economic, environmental, and functional sustainability. This limitation not only impacts the industry’s ability to develop more sustainable designs and evaluate configuration alternatives, but it also inhibits the comparative evaluation of traditional with emerging technologies in wastewater management (e.g., the use of phototrophic microorganisms for energy recovery). In order to address social factors, we have developed a planning and design process for wastewater treatment systems that is centered on a process of continuous stakeholder participation and that is enhanced through communication tools and lessons learned from the social sciences literature. To provide stakeholders with the a broader set of information in the context of WWTP design, we have also integrated state of the art tools to assess the performance, cost, and life cycle environmental impacts of WWTP designs. Although these tools have been developed independent of one another in the literature, their integration creates opportunities to elucidate tensions and synergistic relationships among goals for sustainability. Ultimately, this methodology and the case study used for its demonstration offer insight into broader themes of WWTP sustainability, improve designs in multiple dimensions, and provide a framework to evaluate emerging technologies in wastewater management. Finally, we have developed a phototrophic process model to predict the performance of phototrophic microorganisms as an energy recovery technology. Through these efforts, this dissertation advances the sustainability of wastewater treatment systems by facilitating sustainable design and decision-making in the context of WWTP design and operation.Ph.D.Environmental EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91603/1/jsguest_1.pd

Decision support systems (DSS) for wastewater treatment plants: a review of the state of the art

The use of decision support systems (DSS) allows integrating all the issues related with sustainable developmentin view of providing a useful support to solve multi-scenario problems. In this work an extensive review on theDSSs applied to wastewater treatment plants (WWTPs) is presented. The main aim of the work is to provide anupdated compendium on DSSs in view of supporting researchers and engineers on the selection of the mostsuitable method to address their management/operation/design problems. Results showed that DSSs weremostly used as a comprehensive tool that is capable of integrating several data and a multi-criteria perspective inorder to provide more reliable results. Only one energy-focused DSS was found in literature, while DSSs based onquality and operational issues are very often applied to site-specific conditions. Finally, it would be important toencourage the development of more user-friendly DSSs to increase general interest and usability.This work is part of a research project supported by grant of the Italian Ministry of Education, University and Research (MIUR) through the Research project of national interest PRIN2012 (D.M. 28 December 2012 n. 957/Ric – Prot. 2012PTZAMC) entitled “Energy consumption and Greenhouse Gas (GHG) emissions in the wastewater treatment plants: a decision support system for planning and management – http://ghgfromwwtp.unipa.it” in which the first author is the Principal Investigator. In addition, some coauthors acknowledge the partial support of the Industrial Doctorate Programme (2017-DI-006) and the Research Consolidated Groups/Centres Grant (2017 SGR 574) from the Catalan Agency of University and Research Grants Management (AGAUR), from Catalan Government.Peer ReviewedPostprint (author's final draft