Superstructure Optimization of Petroleum Refinery Design: Processing Alternatives for Naphtha Produced from the Atmospheric Distillation Unit

This research project concerns superstructure optimization for the design of petroleum refineries focusing on the subsystem that considers the alternatives for naphtha produced from the atmospheric distillation unit (ADU). The intricate complexities associated with this process synthesis problem in general and the refinery design problem in specific necessitates the development and implementation of a systematic and automated approach that efficiently and rigorously integrate the elaborate interactions involving the design decision variables. The primary objective of this research is to establish a systematic procedure to determine the optimal topology of the refinery subsystem of naphtha produced from the ADUusing the optimization or mathematical programming approach. Through the identification of equipment, raw materials, products, and process alternatives in terms of the different feasible choices of states (material streams) and tasks (process units) for the mentioned subsystem, the first step is to represent the problem as the interconnections between these elements in a network representation of a superstructure. Subsequently, an optimization model is formulated with binary and continuous variables in order to arrive at the optimum flowsheet design. The scope of this work is focused on the formulation of a mixed-integer linear programming (MILP) and a generalized disjunctive programming (GDP) optimization models. The independent design decision variables are flows of materials and the continuous variables of stream flowrates with the discrete variables denoting the existence of streams. Logical constraints are extensively incorporated in the models to represent qualitative design knowledge through design specifications and structural specifications on the interconnectivity relationships involving the states and the tasks. Computational studies to demonstrate the implementation of the proposed modeling approaches are carried out on the GAMS modeling language platform using the established GAMS/CPLEX solver and the new code of GAMS/LOGMIP solver for the MILP and GDP, respectively. Two design scenarios are considered as distinguished by the API gravity (specific gravity) of the crude charge to the ADU. The optimal refinery topology generated from the MILP and GDP model agree with the typical existing refinery topology. The way forward for this project is to account for varying sulphur content in the crude charge as well as to introduce nonlinearity in the composition modeling to obtain a more practical representation of a real-world refinery design problem

Logical topology design for IP rerouting: ASONs versus static OTNs

IP-based backbone networks are gradually moving to a network model consisting of high-speed routers that are flexibly interconnected by a mesh of light paths set up by an optical transport network that consists of wavelength division multiplexing (WDM) links and optical cross-connects. In such a model, the generalized MPLS protocol suite could provide the IP centric control plane component that will be used to deliver rapid and dynamic circuit provisioning of end-to-end optical light paths between the routers. This is called an automatic switched optical (transport) network (ASON). An ASON enables reconfiguration of the logical IP topology by setting up and tearing down light paths. This allows to up- or downgrade link capacities during a router failure to the capacities needed by the new routing of the affected traffic. Such survivability against (single) IP router failures is cost-effective, as capacity to the IP layer can be provided flexibly when necessary. We present and investigate a logical topology optimization problem that minimizes the total amount or cost of the needed resources (interfaces, wavelengths, WDM line-systems, amplifiers, etc.) in both the IP and the optical layer. A novel optimization aspect in this problem is the possibility, as a result of the ASON, to reuse the physical resources (like interface cards and WDM line-systems) over the different network states (the failure-free and all the router failure scenarios). We devised a simple optimization strategy to investigate the cost of the ASON approach and compare it with other schemes that survive single router failures

Active Learning of Multiple Source Multiple Destination Topologies

We consider the problem of inferring the topology of a network with $M$ sources and $N$ receivers (hereafter referred to as an $M$-by-$N$ network), by sending probes between the sources and receivers. Prior work has shown that this problem can be decomposed into two parts: first, infer smaller subnetwork components (i.e., $1$-by-$N$'s or $2$-by-$2$'s) and then merge these components to identify the $M$-by-$N$ topology. In this paper, we focus on the second part, which had previously received less attention in the literature. In particular, we assume that a $1$-by-$N$ topology is given and that all $2$-by-$2$ components can be queried and learned using end-to-end probes. The problem is which $2$-by-$2$'s to query and how to merge them with the given $1$-by-$N$, so as to exactly identify the $2$-by-$N$ topology, and optimize a number of performance metrics, including the number of queries (which directly translates into measurement bandwidth), time complexity, and memory usage. We provide a lower bound, $\lceil \frac{N}{2} \rceil$, on the number of $2$-by-$2$'s required by any active learning algorithm and propose two greedy algorithms. The first algorithm follows the framework of multiple hypothesis testing, in particular Generalized Binary Search (GBS), since our problem is one of active learning, from $2$-by-$2$ queries. The second algorithm is called the Receiver Elimination Algorithm (REA) and follows a bottom-up approach: at every step, it selects two receivers, queries the corresponding $2$-by-$2$, and merges it with the given $1$-by-$N$; it requires exactly $N-1$ steps, which is much less than all $\binom{N}{2}$ possible $2$-by-$2$'s. Simulation results over synthetic and realistic topologies demonstrate that both algorithms correctly identify the $2$-by-$N$ topology and are near-optimal, but REA is more efficient in practice

Optimized Design of Survivable MPLS over Optical Transport Networks. Optical Switching and Networking

In this paper we study different options for the survivability implementation in MPLS over Optical Transport Networks in terms of network resource usage and configuration cost. We investigate two approaches to the survivability deployment: single layer and multilayer survivability and present various methods for spare capacity allocation (SCA) to reroute disrupted traffic. The comparative analysis shows the influence of the traffic granularity on the survivability cost: for high bandwidth LSPs, close to the optical channel capacity, the multilayer survivability outperforms the single layer one, whereas for low bandwidth LSPs the single layer survivability is more cost-efficient. For the multilayer survivability we demonstrate that by mapping efficiently the spare capacity of the MPLS layer onto the resources of the optical layer one can achieve up to 22% savings in the total configuration cost and up to 37% in the optical layer cost. Further savings (up to 9 %) in the wavelength use can be obtained with the integrated approach to network configuration over the sequential one, however, at the increase in the optimization problem complexity. These results are based on a cost model with actual technology pricing and were obtained for networks targeted to a nationwide coverage