Optimization of refinery preheat trains undergoing fouling: control, cleaning scheduling, retrofit and their integration

Crude refining is one of the most energy intensive industrial operations. The large amounts of crude processed, various sources of inefficiencies and tight profit margins promote improving energy recovery. The preheat train, a large heat exchanger network, partially recovers the energy of distillation products to heat the crude, but it suffers of the deposition of material over time – fouling – deteriorating its performance. This increases the operating cost, fuel consumption, carbon emissions and may reduce the production rate of the refinery. Fouling mitigation in the preheat train is essential for a profitable long term operation of the refinery. It aims to increase energy savings, and to reduce operating costs and carbon emissions. Current alternatives to mitigate fouling are based on heuristic approaches that oversimplify the representation of the phenomena and ignore many important interactions in the system, hence they fail to fully achieve the potential energy savings. On the other hand, predictive first principle models and mathematical programming offer a comprehensive way to mitigate fouling and optimize the performance of preheat trains overcoming previous limitations. In this thesis, a novel modelling and optimization framework for heat exchanger networks under fouling is proposed, and it is based on fundamental principles. The models developed were validated against plant data and other benchmark models, and they can predict with confidence the main effect of operating variables on the hydraulic and thermal performance of the exchangers and those of the network. The optimization of the preheat train, an MINLP problem, aims to minimize the operating cost by: 1) dynamic flow distribution control, 2) cleaning scheduling and 3) network retrofit. The framework developed allows considering these decisions individually or simultaneously, although it is demonstrated that an integrated approach exploits the synergies among decision levels and can reduce further the operating cost. An efficient formulation of the model disjunctions and time representation are developed for this optimization problem, as well as efficient solution strategies. To handle the combinatorial nature of the problem and the many binary decisions, a reformulation using complementarity constraints is proposed. Various realistic case studies are used to demonstrate the general applicability and benefits of the modelling and optimization framework. This is the first time that first principle predictive models are used to optimize various types of decisions simultaneously in industrial size heat exchanger networks. The optimization framework developed is taken further to an online application in a feedback loop. A multi-loop NMPC approach is designed to optimize the flow distribution and cleaning scheduling of preheat trains over two different time scales. Within this approach, dynamic parameter estimation problems are solved at frequent intervals to update the model parameters and cope with variability and uncertainty, while predictive first principle models are used to optimize the performance of the network over a future horizon. Applying this multi-loop optimization approach to a case study of a real refinery demonstrates the importance of considering process variability on deciding about optimal fouling mitigation approaches. Uncertainty and variability have been ignored in all previous model based fouling mitigation strategies, and this novel multi-loop NMPC approach offers a solution to it so that the economic savings are enhanced. In conclusion, the models and optimization algorithms developed in this thesis have the potential to reduce the operating cost and carbon emission of refining operations by mitigating fouling. They are based on accurate models and deterministic optimization that overcome the limitations of previous applications such as poor predictability, ignoring variability and dynamics, ignoring interactions in the system, and using inappropriate tools for decision making.Open Acces

Optimal Design of Hybrid Membrane Networks for Wastewater Treatment

Water consumption and wastewater generation depletes water resources and has a destructive impact on the environment. Recent attention has aimed at preserving water resources and preventing pollution through several routes. Restrictions on wastewater discharge into the environment, recycling, reuse and regeneration of wastewater streams are now common practices toward achieving these objectives. Membrane and integrated membrane processes have been shown to be effective at reducing water usage and recovering valuable compounds. This thesis focuses on topics related to the optimal synthesis of wastewater treatment networks by hybrid membrane systems. The use of superstructures has been a useful tool to synthesize chemical engineering process flowsheets. The approach postulates all possible alternatives of a potential treatment network. Within the representation, an optimal solution is assumed to be hidden in the given superstructure. State space is a framework to process synthesis problems which involves heat and mass exchange. In this representation, unit operations, utility units and utility streams can be embedded in such a way that all the process synthesis alternatives can be realized. Such a framework can be applied for water and wastewater synthesis problems. Several research optimization studies presented membrane and hybrid membrane process synthesis problems for wastewater treatment. Nonetheless, the problems in fact can be represented in several ways. Therefore, the mathematical programs are expected to be different for every postulated representation. Comparison between different representations and their mathematical programs are analyzed to highlight the relationship between the superstructure representation and their mathematical programming formulations. Possible improvement of these superstructures is addressed. Also, a generic representation is provided to give a systematic and clear description for assembling hybrid membrane system superstructures via the state space approach. The synthesis of reverse osmosis networks (RON) for water and wastewater treatment network is presented as a superstructure problem. The mathematical programming model describes the RON through a nonconvex mixed integer nonlinear program (MINLP). A mixed integer linear program (MILP) is derived based on the convex relaxation of the nonconvex terms in the MINLP to bound the global optimum. The MILP models are solved iteratively to supply different initial guesses for the nonconvex MINLP model. It is found that such a procedure is effective in finding local optimum solutions in reasonable time. Water desalination and treatment of aqueous wastes from the pulp and paper industry are considered as case studies to illustrate the solution strategy. The RON mathematical program is a nonconvex MINLP which contains several local optima. A deterministic branch and bound (B&B) algorithm to determine the global optimum for the RON synthesis problem has also been developed. A piecewise MILP is derived based on the convex relaxation of the nonconvex terms present in the MINLP formulation to approximate the original nonconvex program and to obtain a valid lower bound on the global optimum. The MILP model is solved at every node in the branch and bound tree to verify the global optimality of the treatment network within a pre-specified gap tolerance. Several constraints are developed to simultaneously screen the treatment network alternatives during the search, tighten the variable bounds and consequently accelerate algorithm convergence. Water desalination is considered as a case study to illustrate this approach for global optimization of the RO network. Wastewater and groundwater streams contaminated with volatile organic compounds (VOCs) require proper treatment to comply with discharge standards or drinking requirement restrictions. Air stripping and pervaporation are two common treatment technologies for water streams contaminated with VOCs. The combination of these technologies for water treatment which are representative of hybrid membrane systems may offer advantages over stand-alone treatments. Superstructure optimization uses the framework of hybridization to determine the optimal treatment network and the optimal operational requirements for the treatment units to achieve desired water qualities. Two case studies are presented to illustrate the proposed approach and sensitivity of the optimal solutions to given perturbations is analyzed

Optimization of integrated water and multiregenerator membrane systems

A thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy (Chemical Engineering), September 2017Water and energy are key resources in the process industry. The water-energy nexus considers the interdependence of water and energy resources and their effect on the environment. The increasing awareness of environmental regulations has heightened the need for process integration techniques that are environmentally benign and economically feasible. Process integration techniques within water network synthesis require a holistic approach for the sustainable use of water through reuse and recycle and regeneration reuse and recycle. Conventional methods for water minimisation through water network synthesis often use the “black-box” approach to represent the performance of the regenerators. The degree of contaminant removal and cost of regeneration are represented by linear functions. This, therefore, leads to suboptimal operating conditions and inaccurate cost representation of the regeneration units. This work proposes a robust water network superstructure optimisation approach for the synthesis of a multi-regenerator network for the simultaneous minimisation of water and energy. Two types of membrane regenerators are considered for this work, namely, electrodialysis and reverse osmosis. Detailed models of the regeneration units are embedded into the water network superstructure optimisation model to simultaneously minimise water, energy, operating and capital costs. The presence of continuous and integer variables, as well as nonlinear constraints renders the problem a mixed integer nonlinear program (MINLP). The developed model is applied to two illustrative examples involving a single contaminant and multiple contaminants and one industrial case study of a power utility plant involving a single contaminant to demonstrate its applicability. The application of the model to the single contaminant illustrative example lead to a 43.7% freshwater reduction, 50.9% decrease in wastewater generation and 46% savings in total water network cost. The multi-contaminant illustrative example showed 11.6% freshwater savings, 15.3% wastewater reduction, 57.3% savings in regeneration and energy cost compared to the water network superstructure with “black-box” regeneration model. The industrial case study showed a savings of up to 18.7% freshwater consumption, 82.4% wastewater reduction and up to 17% savings on total water network cost.XL201

GALINI: an extensible solver for mixed-integer quadratically-constrained problems

Many industrial relevant optimization problems can be formulated as Mixed-Integer Quadratically Constrained Problems. This class of problems are difficult to solve because of 1) the non-convex bilinear terms 2) integer variables. This thesis develops the Python library \suspect{} for detecting special structure (monotonicity and convexity) of Pyomo models. This library can be extended to provide specialized detection for complex expressions. As a motivating example, we show how the library can be used to detect the convexity of the reciprocal of the log mean temperature difference. This thesis introduces GALINI: a novel solver that is easy to extend at runtime with new 1) cutting planes, 2) primal heuristics, 3) branching strategies, 4) node selection strategies, and 5) relaxations.GALINI uses Pyomo to represent optimization problems, this decision makes it possible to integrate with the existing Pyomo ecosystem to provide, for example, building blocks for relaxations. We test the solver on two large datasets and show that the performance is comparable to existing open source solvers. Finally, we present a library to formulate pooling problems, a class of network flow problems, using Pyomo. The library provides a mechanism to automatically generate the PQ-formulation for pooling problems. Since the library keeps the knowledge of the original network, it can 1) use a mixed-integer programming primal heuristic specialized for the pooling problem to find a feasible solution, and 2) generate valid cuts for the pooling problem. We use this library to develop an extension for GALINI that uses the mixed-integer programming primal heuristic to find a feasible solution and that generates cuts at every node of the branch & cut algorithm. We test GALINI with the pooling extensions on large scale instances of the pooling problem and show that we obtain results that are comparable to or better than the best available commercial solver on dense instances.Open Acces

Modeling and computational issues in the development of batch processes

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 1997.Includes bibliographical references (p. 385-401).by Russell John Allgor.Ph.D

Man-portable power generation devices : product design and supporting algorithms

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 2006.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 351-380).A methodology for the optimal design and operation of microfabricated fuel cell systems is proposed and algorithms for relevant optimization problems are developed. The methodology relies on modeling, simulation and optimization at three levels of modeling detail. The first class of optimization problems considered are parametric mixed-integer linear programs and the second class are bilevel programs with nonconvex inner and outer programs; no algorithms exist currently in the open literature for the global solution of either problem in the form considered here. Microfabricated fuel cell systems are a promising alternative to batteries for manportable power generation. These devices are potential consumer products that comprise a more or less complex chemical process, and can therefore be considered chemical products. With current computational possibilities and available algorithms it is impossible to solve for the optimal design and operation in one step since the devices considered involve complex geometries, multiple scales, time-dependence and parametric uncertainty. Therefore, a methodology is presented based on decomposition into three levels of modeling detail, namely system-level models for process synthesis,(cont.) intermediate fidelity models for optimization of sizes and operation, and detailed, computational fluid dynamics models for geometry improvement. Process synthesis, heat integration and layout considerations are addressed through the use of lumped algebraic models, general enough to be independent of detailed design choices, such as reactor configuration and catalyst choice. Through the use of simulation and parametric mixed-integer optimization the most promising process structures along with idealized layouts are selected among thousands of alternatives. At the intermediate fidelity level space-distributed models are used, which allow optimization of unit sizes and operation for a given process structure without the need to specify a detailed geometry. The resulting models involve partial differential-algebraic equations and dynamic optimization is employed as the solution technique. Finally, the use of detailed two- and three-dimensional computational fluid dynamics facilitates geometrical improvements as well as the derivation and validation of modeling assumptions that are employed in the system-level and intermediate fidelity models. Steady-state case studies are presented assuming a constant power demand;(cont.) the methodology can be also applied to transient considerations and the case of variable power demand. Parametric programming provides the solution of an optimization problem, the data of which depend on one or many unknown real-valued parameters, for each possible value of the parameter(s). In this thesis mixed-integer linear programs are considered, i.e., optimization programs with affine functions involving real- and integervalued variables. In the first part the multiparametric cost-vector case is considered, i.e., an arbitrary finite number of parameters is allowed, that influence only the coefficients of the objective function. The extension of a well-known algorithm for the single-parameter case is presented, and the algorithm behavior is illustrated on simple examples with two parameters. The optimality region of a given basis is a polyhedron in the parameter space, and the algorithm relies on progressively constructing these polyhedra and solving mixed-integer linear programs at their vertices. Subsequently, two algorithmic alternatives are developed, one based on the identification of optimality regions, and one on branch-and-bound. In the second part the single-parameter general case is considered,(cont.) i.e., a single parameter is allowed that can simultaneously influence the coefficients of the objective function, the right-hand side of the constraints, and also the coefficients of the matrix. Two algorithms for mixed-integer linear programs are proposed. The first is based on branch-and-bound on the integer variables, solving a parametric linear program at each node, and the second is based on decomposition of the parametric optimization problem into a series of mixed-integer linear and mixed-integer nonlinear optimization problems. For the parametric linear programs an improvement of a literature algorithm for the solution of linear programs based on rational operations is presented and an alternative based on predictor-continuation is proposed. A set of test problems is introduced and numerical results for these test problems are discussed. The algorithms are then applied to case studies from the man-portable power generation. Finally extensions to the nonlinear case are discussed and an example from chemical equilibrium is analyzed. Bilevel programs are hierarchical programs where an outer program is constrained by an embedded inner program.(cont.) Here the co-operative formulation of inequality constrained bilevel programs involving real-valued variables and nonconvex functions in both the inner and outer programs is considered. It is shown that previous literature proposals for the global solution of such programs are not generally valid for nonconvex inner programs and several consequences of nonconvexity in the inner program are identified. Subsequently, a bounding algorithm for the global solution is presented. The algorithm is rigorous and terminates finitely to a solution that satisfies e-optimality in the inner and outer programs. For the lower bounding problem, a relaxed program, containing the constraints of the inner and outer programs augmented by a parametric upper bound on the optimal solution function of the inner program, is solved to global optimality. For the case that the inner program satisfies a constraint qualification, a heuristic for tighter lower bounds is presented based on the KKT necessary conditions of the inner program. The upper bounding problem is based on probing the solution obtained in the lower bounding procedure. Branching and probing are not required for convergence but both have potential advantages.(cont.) Three branching heuristics are described and analyzed. A set of test problems is introduced and numerical results for these test problems and for literature examples are presented.by Alexander Mitsos.Ph.D

Integrated Chemical Processes in Liquid Multiphase Systems

The essential principles of green chemistry are the use of renewable raw materials, highly efficient catalysts and green solvents linked with energy efficiency and process optimization in real-time. Experts from different fields show, how to examine all levels from the molecular elementary steps up to the design and operation of an entire plant for developing novel and efficient production processes