Decision Support System for Biotech Processes
Venue:-11:00 am on 27 July 2020
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Abstract :- Biopharmaceuticals are therapeutic drugs driven from biological means such as monoclonal antibodies, engineered protein hormones, cytokines and vaccines. They are successful in curing autoimmune diseases by manipulating patient genetics with fewer side effects compared to traditional chemical drugs. As the biopharmaceutical sector has matured, pressure is increasing for design of flexible process configurations that can cope up process variability associated with the manufacturing of biopharmaceuticals. Due to biological nature of the reactants, it is challenging to maintain required purity of final product and other quality control measures. Under the risk of high clinical failure linked to drug development coupled with greater process variability, design of decision support system for multi-product facility in biopharma remains a challenge. Computer aided decision support systems have been used to assist decision making in the past for the biopharmaceutical process using heuristic approaches i.e. production planning and scheduling, facility design, debottlenecking and capacity analysis. A lot of work has been done towards planning, scheduling and optimization in process systems engineering (PSE), and recently increasing efforts are being made to understand process design and optimization in the biopharmaceutical industry. More work is required to get clearer, deeper and better understanding about process design and optimization in this field. Therefore, the purpose of present study is to conduct research and develop novel models to fill the gap between computer aided process design tools and mathematical optimization and scheduling.
The first objective of present work is to propose an extension of earlier unit specific event based literature model to address the observed model inconsistencies such as early product delivery, no initial setup time, no proper mapping of upstream and downstream tasks, and storage sequencing. Then the second part of the objective is to extend the work for an improved model with features such as minimum campaign length occurring over consecutive multiple events, shelf-life, material balance, sales and penalty constraints, and new initial setup sequencing constraints. Then it is extended for handling early delivery case by providing new material balance and sales and penalty constraints. Improved mixed integer linear programming (MILP) models have been formulated in this work for multi-stage, multi-product, mid-term planning of biopharmaceutical manufacturing, giving better results compared to the literature.
Chromatography is a popular analytical technique for purification and separation of desired protein from the mixture of cell broth. It has major impact on the purity attributes of the final product in bio-pharmaceuticals industry. Limited lifetime and higher cost of chromatography resins makes it necessary to get the maximum yield from the process step. Chromatography resins generally contribute to 50% cost of the process in biopharmaceutical manufacturing due to which these resins are reused multiple times. Therefore, maintenance (i.e. restoration of resins capability and performance to its initial level) is one of the most important decisions for such kind of processes. So, when to plan the maintenance operation subject to performance decay is the critical issue in the bioprocess facility design and scheduling. Inconsistencies such as real time storage violation and early products delivery have been observed in the literature models. A STN framework based, unit specific event based model is proposed for performance decay of chromatography resin. The proposed model is implemented on an industrial case study with two different types of resins. The proposed model shows improvement over existing literature results.
In the third part of the present study, the purpose is to conduct research and develop novel mathematical models to fill the gap between computer aided process design tools such as SuperPro® and SchedulePro® and mathematical optimization and scheduling. To explore techno-economic benefits, if any, some examples adopted from SchedulePro® library, on a microbial biopharmaceutical process, are modelled and validated using mathematical programming based approaches. A unit specific event based model is proposed with and without utility constraints which shows improvement over the heuristic approaches. Five different literature case studies are implemented using the model.
In the fourth part of the present work scheduling for the production of Lethal Toxin Neutralizing Factor (LTNF) and Granulocyte Colony-Stimulating Factor (GCSF) production are generated where CIP (cleaning in progress) and some other units are shared.
Overall, the contributions from this work include improvements over the literature model to handle model inconsistencies such as no initial setup time, unwanted early delivery of final products and no proper mapping of upstream and downstream tasks. The issue of minimum campaign length occurring over consecutive multiple events is modelled. A model for the dedicated early delivery case is proposed. A unit specific event based model for maintenance scheduling under performance decay of chromatography resins is proposed which shows improvement over the literature models using discrete events and global events. Two unit specific event based models are proposed for batch processing to make comparison between heuristic approaches versus mathematical models based on with and without utility constraints, resulting in improved objective values over the heuristic based solutions.