Optimal Antibody Purification Strategies Using Data-Driven Models

This work addresses the multiscale optimization of the purification processes of antibody fragments. Chromatography decisions in the manufacturing processes are optimized, including the number of chromatography columns and their sizes, the number of cycles per batch, and the operational flow velocities. Data-driven models of chromatography throughput are developed considering loaded mass, flow velocity, and column bed height as the inputs, using manufacturing-scale simulated datasets based on microscale experimental data. The piecewise linear regression modeling method is adapted due to its simplicity and better prediction accuracy in comparison with other methods. Two alternative mixed-integer nonlinear programming (MINLP) models are proposed to minimize the total cost of goods per gram of the antibody purification process, incorporating the data-driven models. These MINLP models are then reformulated as mixed-integer linear programming (MILP) models using linearization techniques and multiparametric disaggregation. Two industrially relevant cases with different chromatography column size alternatives are investigated to demonstrate the applicability of the proposed models

An MILP model for safe multi-floor process plant layout using the domino hazard index

In this paper, an optimisation-based approach to obtain safe multi-floor process plant layout designs using the domino hazard index (a sub-index of the integrated inherent safety index) is presented. A mixed integer linear programming (MILP) model is proposed to obtain the economically optimal multi-floor layout design considering connection by pipes, horizontal and vertical pumping of process fluids, purchase of land, fixed and area-dependent construction of floors, the financial risk associated with hazardous events and their escalation potential, and the installation of passive protection devices. Hazardous events such as pool fires, jet fires, flash fires, fireballs and blast waves resulting from explosions are considered using a novel and more realistic estimation of safety distances between equipment items. A bi-objective optimisation problem is also considered, minimising the layout costs and the total domino hazard index values for the plant, adopting the ϵ-constraint method. The proposed model is then applied to an 11-unit case study susceptible to each of these hazardous events, obtaining results with the optimal layout and protection device configurations in a relatively short amount of time

Optimal multi-floor process plant layout with production sections

This paper addresses the multi-floor process plant layout problem by developing four mixed integer linear programming (MILP) models. The problem involves decisions concerning the optimal spatial arrangement of process plant equipment and/or auxiliary units considering equipment connectivity, pumping and construction costs, and other factors. These considerations are extended to account for tall equipment items that span across floors and the availability of predefined production sections. The proposed models determine simultaneously the number of floors per section, floor areas per section, plot layout and site layout, and are applied to two case studies with up to 22 units and 6 production sections to demonstrate their applicability

Mathematical programming for piecewise linear regression analysis

In data mining, regression analysis is a computational tool that predicts continuous output variables from a number of independent input variables, by approximating their complex inner relationship. A large number of methods have been successfully proposed, based on various methodologies, including linear regression, support vector regression, neural network, piece-wise regression, etc. In terms of piece-wise regression, the existing methods in literature are usually restricted to problems of very small scale, due to their inherent non-linear nature. In this work, a more efficient piece-wise linear regression method is introduced based on a novel integer linear programming formulation. The proposed method partitions one input variable into multiple mutually exclusive segments, and fits one multivariate linear regression function per segment to minimise the total absolute error. Assuming both the single partition feature and the number of regions are known, the mixed integer linear model is proposed to simultaneously determine the locations of multiple break-points and regression coefficients for each segment. Furthermore, an efficient heuristic procedure is presented to identify the key partition feature and final number of break-points. 7 real world problems covering several application domains have been used to demonstrate the efficiency of our proposed method. It is shown that our proposed piece-wise regression method can be solved to global optimality for datasets of thousands samples, which also consistently achieves higher prediction accuracy than a number of state-of-the-art regression methods. Another advantage of the proposed method is that the learned model can be conveniently expressed as a small number of if-then rules that are easily interpretable. Overall, this work proposes an efficient rule-based multivariate regression method based on piece-wise functions and achieves better prediction performance than state-of-the-arts approaches. This novel method can benefit expert systems in various applications by automatically acquiring knowledge from databases to improve the quality of knowledge base

Neural stem cell transplantation in a rat model of intracerebral haemorrhage plus haematoma aspiration

INTRODUCTION: Cell replacement therapy holds great potential for brain tissue repair following intracerebral haemorrhage (ICH). Haematoma evacuation alleviates the mass effect and prevents the secondary pathological processes. This study was conducted to investigate the survival and differentiation of neural stem cells (NSCs) after transplantation into the brain cavity following haematoma aspiration in adult male Sprague-Dawley rats …published_or_final_versio

Multi-objective optimisation for biopharmaceutical manufacturing under uncertainty

This work addresses the multi-objective optimisation of manufacturing strategies of monoclonal antibodies under uncertainty. The chromatography sequencing and column sizing strategies, including resin at each chromatography step, number of columns, column diameters and bed heights, and number of cycles per batch, are optimised. The objective functions simultaneously minimise the cost of goods per gram and maximise the impurity reduction ability of the purification process. Three parameters are treated as uncertainties, including bioreactor titre, and chromatography yield and capability to remove impurities. Using chance constraint programming techniques, a multi-objective mixed integer optimisation model is proposed. Adapting both ε-constraint method and Dinkelbach's algorithm, an iterative solution approach is developed for Pareto-optimal solutions. The proposed model and approach are applied to an industrially-relevant example, demonstrating the benefits of the proposed model through Monte Carlo simulation. The sensitivity analysis of the confidence levels used in the chance constraints of the proposed model is also conducted

Fair profit distribution in multi-echelon supply chains via transfer prices

The total profit maximisation of a supply chain may result in an uneven and impractical profit distribution among the members. This work addresses the fair profit distribution within a multi-echelon supply chain using transfer prices. A mixed integer linear programming (MILP) model framework is proposed for the optimal production, distribution and capacity planning of a supply chain of an active ingredient (AI), consisting of AI plants, formulation plants and markets. The transfer prices of the AI from AI plants to formulation plants, and those of products from formulation plants to markets are to be optimised. The proportional and max–min fairness criteria are adopted to define fair profit distributions. Considering bargaining powers of supply chain members, game theoretic solution approaches are developed for fair solutions using Nash bargaining and lexicographic maximin principles. Especially, a hierarchical approach is developed to obtain an approximate optimal fair solution efficiently. The applicability and efficiency of the proposed approaches are demonstrated by two examples, including a real world agrochemical supply chain

Optimisation-based Framework for Resin Selection Strategies in Biopharmaceutical Purification Process Development

This work addresses rapid resin selection for integrated chromatographic separations when conducted as part of a high-throughput screening (HTS) exercise during the early stages of purification process development. An optimisation-based decision support framework is proposed to process the data generated from microscale experiments in order to identify the best resins to maximise key performance metrics for a biopharmaceutical manufacturing process, such as yield and purity. A multiobjective mixed integer nonlinear programming (MINLP) model is developed and solved using the ε-constraint method. Dinkelbach's algorithm is used to solve the resulting mixed integer linear fractional programming (MILFP) model. The proposed framework is successfully applied to an industrial case study of a process to purify recombinant Fc Fusion protein from low molecular weight and high molecular weight product related impurities, involving two chromatographic steps with 8 and 3 candidate resins for each step, respectively. The computational results show the advantage of the proposed framework in terms of computational efficiency and flexibility. This article is protected by copyright. All rights reserved

Optimisation approaches for supply chain planning and scheduling under demand uncertainty

This work presents efficient MILP-based approaches for the planning and scheduling of multiproduct multistage continuous plants with sequence-dependent changeovers in a supply chain network under demand uncertainty and price elasticity of demand. This problem considers multiproduct plants, where several products must be produced and delivered to supply the distribution centres (DCs), while DCs are in charge of storing and delivering these products to the final markets to be sold. A hybrid discrete/continuous model is proposed for this problem by using the ideas of the Travelling Salesman Problem (TSP) and global precedence representation. In order to deal with the uncertainty, we proposed a Hierarchical Model Predictive Control (HMPC) approach for this particular problem. Despite of its efficiency, the final solution reported still could be far from the global optimum. Due to this, Local Search (LS) algorithms are developed to improve the solution of HMPC by rescheduling successive products in the current schedule. The effectiveness of the proposed solution techniques is demonstrated by solving a large-scale instance and comparing the solution with the original MPC and a classic Cutting Plane approach adapted for this work

A mixed integer linear programming model for the optimal operation of a network of gas oil separation plants

Inspired from a real case study of a Saudi oil company, this work addresses the optimal operation of a regional network of gas oil separation plants (GOSPs) in Arabian Gulf Coast Area to ultimately achieve higher savings in operating expenditures (OPEX) than those achieved by adopting single-surface facility optimisation. An originally tailored and integrated mixed integer linear programming (MILP) model is proposed to optimise the crude transfer through swing pipelines and equipment utilisation in each GOSP, to minimise the operating costs of a network of GOSPs. The developed model is applied to an existing network of GOSPs in the Ghawar field, Saudi Arabia, by considering 12 different monthly production scenarios developed from real production rates. Compared to rule-based current practice, an average 12.8% cost saving is realised by the developed model