Designing cost‐effective biopharmaceutical facilities using mixed‐integer optimization

Chromatography operations are identified as critical steps in a monoclonal antibody (mAb) purification process and can represent a significant proportion of the purification material costs. This becomes even more critical with increasing product titers that result in higher mass loads onto chromatography columns, potentially causing capacity bottlenecks. In this work, a mixed‐integer nonlinear programming (MINLP) model was created and applied to an industrially relevant case study to optimize the design of a facility by determining the most cost‐effective chromatography equipment sizing strategies for the production of mAbs. Furthermore, the model was extended to evaluate the ability of a fixed facility to cope with higher product titers up to 15 g/L. Examination of the characteristics of the optimal chromatography sizing strategies across different titer values enabled the identification of the maximum titer that the facility could handle using a sequence of single column chromatography steps as well as multi‐column steps. The critical titer levels for different ratios of upstream to dowstream trains where multiple parallel columns per step resulted in the removal of facility bottlenecks were identified. Different facility configurations in terms of number of upstream trains were considered and the trade‐off between their cost and ability to handle higher titers was analyzed. The case study insights demonstrate that the proposed modeling approach, combining MINLP models with visualization tools, is a valuable decision‐support tool for the design of cost‐effective facility configurations and to aid facility fit decisions. © 2013 The Authors. Published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 29:1472–1483, 2013     

Modelling the learning of biomechanics and visual planning for decision-making of motor actions

Recent experiments showed that the bio-mechanical ease and end-point stability associated to reaching movements are predicted prior to movement onset, and that these factors exert a significant influence on the choice of movement. As an extension of these results, here we investigate whether the knowledge about biomechanical costs and their influence on decision-making are the result of an adaptation process taking place during each experimental session or whether this knowledge was learned at an earlier stage of development. Specifically, we analysed both the pattern of decision-making and its fluctuations during each session, of several human subjects making free choices between two reaching movements that varied in path distance (target relative distance), biomechanical cost, aiming accuracy and stopping requirement. Our main result shows that the effect of biomechanics is well established at the start of the session, and that, consequently, the learning of biomechanical costs in decision-making occurred at an earlier stage of development. As a means to characterise the dynamics of this learning process, we also developed a model-based reinforcement learning model, which generates a possible account of how biomechanics may be incorporated into the motor plan to select between reaching movements. Results obtained in simulation showed that, after some pre-training corresponding to a motor babbling phase, the model can reproduce the subjects’ overall movement preferences. Although preliminary, this supports that the knowledge about biomechanical costs may have been learned in this manner, and supports the hypothesis that the fluctuations observed in the subjects’ behaviour may adapt in a similar fashion.     

Life‐cycle and cost of goods assessment of fed‐batch and perfusion‐based manufacturing processes for mAbs

Life‐cycle assessment (LCA) is an environmental assessment tool that quantifies the environmental impact associated with a product or a process (e.g., water consumption, energy requirements, and solid waste generation). While LCA is a standard approach in many commercial industries, its application has not been exploited widely in the bioprocessing sector. To contribute toward the design of more cost‐efficient, robust and environmentally‐friendly manufacturing process for monoclonal antibodies (mAbs), a framework consisting of an LCA and economic analysis combined with a sensitivity analysis of manufacturing process parameters and a production scale‐up study is presented. The efficiency of the framework is demonstrated using a comparative study of the two most commonly used upstream configurations for mAb manufacture, namely fed‐batch (FB) and perfusion‐based processes. Results obtained by the framework are presented using a range of visualization tools, and indicate that a standard perfusion process (with a pooling duration of 4 days) has similar cost of goods than a FB process but a larger environmental footprint because it consumed 35% more water, demanded 17% more energy, and emitted 17% more CO₂ than the FB process. Water consumption was the most important impact category, especially when scaling‐up the processes, as energy was required to produce process water and water‐for‐injection, while CO₂ was emitted from energy generation. The sensitivity analysis revealed that the perfusion process can be made more environmentally‐friendly than the FB process if the pooling duration is extended to 8 days. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1324–1335, 2016     

A New Tradeoff Model for Fuzzy Supply Chain Network Design and Optimization

This article proposes a novel mixed integer linear programming model for solving a fuzzy supply chain network (SCN) design problem. This problem includes fuzzy parameters, choosing suppliers according to their quality of raw materials, and the supplier's engagement contracts. There is a tradeoff between raw material quality, and its purchasing and reprocessing costs. If a decision-maker (DM) wishes to work with a supplier that supplies a low-quality raw material, this raw material may be in need of reprocessing. To avoid the reprocessing costs, a supplier that provides a high-quality raw material can be chosen, but in this case the DM faces a high purchasing cost. An integrated fuzzy SCN system that consists of multiple suppliers, manufacturers, distribution centers, and retailers is considered in order to address problems under the aforementioned tradeoffs. Finally, concluding remarks and suggestions for future work are presented.     

Multicriteria Design of Plastic Recycling Based on Quality Information and Environmental Impacts

In this study, we develop a framework for the multicriteria design of plastic recycling based on quality information and environmental impacts for the purpose of supporting collaborative decision making among consumers, municipalities, and recyclers. The subject of this article is the mechanical recycling of postconsumer polyethylene terephthalate (PET) bottles. We present a “quality conversion matrix,” which links the quality of recycled PET resin to the quality of waste PET bottles and operational conditions, described in terms of the functions of modules constituting the entire recycling process. We estimate the quality of recycled PET resin and simulate the applicability to the intended products as the primary criterion by confirming whether the estimated quality of recycled resin satisfies the quality demands of PET resin users. The amounts of carbon dioxide (CO₂) emissions and fossil resource consumption are also estimated as the secondary criteria. An approach to collaborative decision making utilizing mixed‐integer linear programming (MILP) and Monte Carlo simulation is proposed on the premise of different objectives of various stakeholders, where all the feasible optimal solutions for achieving the quality demands are obtained. The quality requirements of waste bottles, along with the CO₂ emissions and fossil resource consumption estimated for each solution, contribute to the collaborative multicriteria design of plastic recycling.