Marcel Joly

Planning and scheduling models for refinery operations

Abstract The main objective of this work is to discuss planning and scheduling applications for refinery operations. Firstly, the development of a nonlinear planning model for refinery production is presented. The model is able to represent a general refinery topology and allows the implementation of nonlinear process models as well as blending relations. Considering the market limitations for each oil derivative usually supplied by the refinery, the optimization model is able to define new operating points, thus increasing the production of more valuable products, while satisfying all specification constraints. Real-world applications are developed for the planning of diesel production in the RPBC refinery in Cubatao (SP, Brazil) among others. The optimization results were compared to the current situation, where no computer algorithm is used and the stream allocation is made based on experience, with the aid of manual calculations. The new operating point represents an increase of several million dollars in annual profitability. The second part of the work addresses scheduling problems in oil refineries that are formulated as mixed integer optimization models and rely on both continuous and discrete time representations. The problem of crude oil inventory management that involves the optimal operation of crude oil unloading from pipelines, transfer to storage tanks and the charging schedule for each crude oil distillation unit will be discussed. Furthermore, the paper will consider the development and solution of optimization models for short-term scheduling of a set of operations that includes: product receiving from processing units, storage and inventory management in intermediate tanks, blending in order to attend oil specifications and demands, and transport sequencing in oil pipelines. Important real-world examples on refinery production and distribution are reported: the diesel distribution problem at RPBC refinery and the production problems related to the fuel oil/asphalt and LPG areas of the REVAP refinery in Sao Jose dos Campos (SP, Brazil), which produces approximately 80% of the national consumption.

Planning and scheduling for petroleum refineries using mathematical programming

The objective of this paper is the development and solution of nonlinear and mixed-integer (MIP) optimization models for real-world planning and scheduling problems in petroleum refineries. Firstly, we present a nonlinear planning model that represents a general refinery topology and allows implementation of nonlinear process models as well as blending relations. The optimization model is able to define new operating points, thus increasing the production of the more valuable products and simultaneously satisfying all specification constraints. The second part addresses scheduling problems in oil refineries, which are formulated as MIP optimization models and rely on both continuous and discrete time representations. Three practical applications closely related to the current refinery scenario are presented. The first one addresses the problem of crude oil inventory management of a refinery that receives several types of crude oil delivered exclusively by a single oil pipeline. Subsequently, two optimization models intended to define the optimal production policy, inventory control and distribution are proposed and solved for the fuel oil and asphalt plant. Finally, the planning model of Moro et al. (1998) is extended in order to sequence decisions at the scheduling level in the liquefied petroleum gas (LPG) area for maximization of the production of petrochemical-grade propane and product delivery.

Optimal control of product quality for batch nylon-6,6 autoclaves

Dynamic optimization problems are usually solved by transforming them into nonlinear programming (NLP) models with either sequential or simultaneous approaches. In this paper, the potential and limitations of both procedures in solving a general batch nylon-6,6 autoclave optimization model are evaluated and discussed. Nylon-6,6 is a highly value-added good produced in large-scale under many different specifications. Highly nonlinear behavior, lack of on-line measurements of polymer qualities and several disturbances inherent to this process motivate its optimal operation. In addition, the operation during the finishing stage of the batch polycondensation is crucial since it largely determines the final product molecular weight and quality. Results show that both strategies can be successfully applied to solve the dynamic optimization problem only after an expressive level of investments in terms of modeling implementation. However, the simultaneous strategy has the advantage that specification constraints can be directly enforced in the model, thus generating better and more robust results.

Modeling interleukin-2-based immunotherapy in AIDS pathogenesis

In this paper, we sought to identify the CD4(+) T-cell dynamics in the course of HIV infection in response to continuous and intermittent intravenous courses of interleukin-2 (IL-2), the principal cytokine responsible for progression of CD4(+) T-lymphocytes from the G1 to the S phase of the cell cycle. Based on multivariate regression models, previous literature has concluded that the increase in survival of CD4(+) T-cell appears to be the critical mechanism leading to sustained CD4(+) T-cell levels in HIV-infected patients receiving intermittent IL-2 therapy. Underscored by comprehensive mathematical modeling, a major finding of the present work is related to the fact that, rather than due to any increase in survival of CD4(+) T-cells, the expressive, selective and sustained CD4(+) T-cell expansions following IL-2 administration may be related to the role of IL-2 in modulating the dynamics of Fas-dependent apoptotic pathways, such as activation-induced cell death (AICD) or HIV-specific apoptotic routes triggered by viral proteins.

The future of computational biomedicine: Complex systems thinking

“More is different” (Philip W. Anderson). Complex systems thinking become instrumental for the modern understanding basis of life sciences in general and, hence, medicine and public health. In this perspective paper, we discuss recent literature and invite readers to explore the utility of complex thinking to properly addressing the constrained-based analysis of high-profile open questions in biomedicine with straightforward implications on public health. Recommendations are then proposed to encourage new multidisciplinary teams to come together in a timely manner in response to novel challenges in the theoretical physiology arena. We conclude that there is the need for far greater attention to the issue of complexity to aptly cope with a new array of problems that would have been unthinkable just a few years ago.

Human immunomodulation and initial HIV spread

Abstract We use published data in order to build up a networked mathematical framework aiming at providing: (a) a predictive understanding on how distinct stressors and immunosenescence may potentially affect the natural inflammatory response mechanisms and (b) new insights to developing early interventions that seek to exploit natural immune processes. Relying on fifty fundamental assumptions on HIV immunopathogenesis, the model simulations suggested that: (a) from a translational perspective, forthcoming physiological transitions in the systemic inflammation process do exist with conditions for bifurcation between the uninfected and the infected state being seriously impacted by immunomodulation and (b) the required therapy efficacy for pre-exposure prophylaxis may be decisively affected by immunomodulation and by the drug class used. Whereas unsuitable to make quantitative predictions due to limited experimental data and the complexity in vivo, this modeling effort paves the way for assessing the impact of personalized medicine for global epidemics within complex systems thinking.