Maintenance can improve the availability of aging production systems and prevent process safety incidents. However, because of system complexity, resource allocation is nontrivial. This research ...developed and applied a framework to obtain optimal future-failure aware and safety-conscious production and maintenance schedules. Ensembles of nonlinear support vector machine classification models were leveraged to predict the time and probability of future equipment failure from equipment condition data. Multiobjective optimization of expected profit and a safety metric was then used to determine optimal process and maintenance schedules. The results of this research were that the ensemble models had an average accuracy and an F1-score of 0.987, that the ensemble models were more accurate and sensitive than the individual classifiers by 3 percentage points, and that the Pareto-optimal process and maintenance schedules were obtained, providing alternative solutions to the decision maker. This research described optimal resource allocation to help improve safety and system effectiveness.
A strategy that calculates an explicit state feedback policy to regulate constrained uncertain discrete‐time uncertain linear systems is presented. We consider uncertain processes, affected by ...box‐bounded multiplicative uncertainty as well as bounded additive uncertainty with linear state and inputs constraints. The proposed method includes (i) the calculation of a terminal set constraint and (ii) the robust reformulation of state constraints in the prediction horizon. These features allow the derivation of the desired policy by solving a single multiparametric quadratic programming problem that guarantees feasible operation in the presence of uncertainty. Additionally, we employ variable and constraint elimination approaches to enhance the computational performance of the strategy. We demonstrate the steps and benefits of these developments with a numerical example and a chemical engineering case study.
In this work, we introduce an approach for the reactive scheduling of production systems with bounded uncertain parameters. The proposed method follows a state-space representation for the scheduling ...problem, and relies on the use of a rolling horizon framework and multiparametric programming techniques. We show that by considering as uncertain parameters the set of variables that describe the state of the system at the beginning of the prediction horizon, we can effectively formulate a set of state-space multiparametric programming problems that are solved just once and off-line. In contrast to existing approaches, the repetitive solution of a new multiparametric problem after each disruptive event is avoided. The results of the parametric optimization are used in a rolling horizon basis without the need for online optimization. The proposed multiparametric programming rolling horizon (mp-RH) approach is applied in the scheduling problem of a network of combined heat and power units (i.e., a unit commitment problem type). Several case studies are solved, potential extensions of the proposed method are provided, and challenging areas wherein research is necessary are discussed.
•Multi-commodity supply chains comprised of modular production units•Developed a “path-based” MILP formulation•Designed an iterative three-phase matheuristic•Preformed computational experiments with ...750 test instances•Order of magnitude performance increase for difficult test instances
Recently, there has been a paradigm shift by certain energy companies towards modular manufacturing, whereby transportable modular production units can be relocated between production facilities to meet the spatial and temporal changes in the availabilities, demands, and prices of the underlying commodities. We refer to the optimal distribution, production, and storage of commodities, including intermediary commodities, and the relocation and operation of the modular production units as the dynamic multiple commodity supply chain problem with modular production units. To this end, we present a “flow-based” and a “path-based” mixed-integer linear programming formulation to model the problem. In an effort to solve large-scale instances of the problem, we propose an iterative three-stage matheuristic for the “path-based” formulation. In the first stage of the matheuristic, a feasible solution to the problem is generated by an integrated column generation and Lagrangian relaxation based heuristic. In the second stage of the matheuristic, a path-relinking procedure is utilized as a local search heuristic to further improve the solution. And in the final stage of the matheuristic, the Lagrangian multipliers are updated via a subgradient method. The effectiveness of the matheuristic is illustrated through numerical experiments with a set of randomly generated test instances. For the large-scale test instances, the results show that the matheuristic produces quality solutions orders of magnitude faster than a “state-of-the-art” mixed-integer linear programming solver.
In this work, we present a novel algorithm for the global solution of tri-level mixed-integer linear optimization problems containing both integer and continuous variables at all three optimization ...levels. Based on multi-parametric theory and our earlier results for bi-level programming problems, the main idea of the algorithm is to recast the lower levels of the tri-level optimization problem as multi-parametric programming problems, in which the optimization variables (continuous and integer) of all the upper level problems, are considered as parameters at the lower levels. The resulting parametric solutions are then substituted into the corresponding higher-level problems sequentially. The algorithm is illustrated through numerical examples, along with implementation and computational studies.
In this paper we present the main foundations and features of an integrated framework and software platform that enables the use of model-based tools in design, operational optimisation and advanced ...control studies. A step-wise procedure is outlined involving (i) the development of a high-fidelity dynamic model, and its validation and model analysis, (ii) a model approximation step, including system identification, model reduction and global sensitivity analysis, (iii) a receding horizon modelling step for model-predictive control (MPC) and reactive scheduling, (iv) a suite of multi-parametric programming techniques for optimisation under uncertainty, explicit/multi-parametric MPC and state-estimation and (v) an ‘in-silico’ validation step for the derived optimisation, control and/or scheduling strategies to be analysed within the original high-fidelity model. The proposed software platform, PAROC, is also introduced and demonstrated in three different classes of process systems engineering applications; a combined heat and power energy system, a distillation column and a periodic purification process for biopharmaceuticals.
•Framework and software platform for design, operational optimisation and control.•Multiparametric programming and explicit model predictive control frame-work.•Model approximation and moving horizon estimation techniques.•Combined heat and power energy system application.•Biopharmaceutical periodic separation system application.
•We developed a spatial MILP model for the optimal solution of GEP problems.•Integration of energy resources management with strategic and tactical decisions.•A real case study of the Greek GEP ...problem was employed.•Greek electricity mix has been in a transition period from lignite to renewables.•Natural gas will bridge the gap towards a cleaner power generation profile.
This paper presents a mixed-integer linear programming (MILP) model for the optimal long-term energy planning of a (national) power generation system. In order to capture more accurately the spatial and technical characteristics of the problem, the underlying geographical area (country) is divided into a number of individual networks that interact with each other. The proposed model determines the optimal planning of the power generation system, the selection of the power generation technologies, the type of fuels and the plant locations so as to meet the expected electricity demand, while satisfying environmental constraints in terms of CO2 emissions. Furthermore, the suggested model determines the electricity imports from neighbouring countries, the electricity transmission as well as the transportation of primary energy resources between domestic networks. A real case study concerning the Greek energy planning problem demonstrates the applicability of the proposed approach, which can provide policy makers with a systematic computer-aided tool to analyse various scenarios and technology options. Finally, a sensitivity analysis was conducted in order to capture the influence of some key parameters such as electricity demand, natural gas and CO2 emission price as well as wind power investment cost.
An ever-growing population together with globally depleting water resources pose immense stresses for water supply systems. Desalination technologies can reduce these stresses by generating fresh ...water from saline water sources. Reverse osmosis (RO), as the industry leading desalination technology, typically involves a complex network of membrane modules that separate unwanted particles from water. The optimal design and operation of these complex RO systems can be computationally expensive. In this work, we present a modeling and optimization strategy for addressing the optimal operation of an industrial-scale RO plant. We employ a feed-forward artificial neural network (ANN) surrogate modeling representation with rectified linear units as activation functions to capture the membrane behavior accurately. Several ANN set-ups and surrogate models are presented and evaluated, based on collected data from the H2Oaks RO desalination plant in South-Central Texas. The developed ANN is then transformed into a mixed-integer linear programming formulation for the purpose of minimizing energy consumption while maximizing water utilization. Trade-offs between the two competing objectives are visualized in a Pareto front, where indirect savings can be uncovered by comparing energy consumption for an array of water recoveries and feed flows.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
The optimal allocation of land for energy generation is of emergent concern due to an increasing demand for renewable power capacity, land scarcity, and the diminishing supply of water. Therefore, ...economically, socially and environmentally optimal design of new energy infrastructure systems require the holistic consideration of water, food and land resources. Despite huge efforts on the modeling and optimization of renewable energy systems, studies navigating the multi-faceted and interconnected food-energy-water-land nexus space, identifying opportunities for beneficial improvement, and systematically exploring interactions and trade-offs are still limited. In this work we present the foundations of a systems engineering decision-making framework for the trade-off analysis and optimization of water and land stressed renewable energy systems. The developed framework combines mathematical modeling, optimization, and data analytics to capture the interdependencies of the nexus elements and therefore facilitate informed decision making. The proposed framework has been adopted for a water-stressed region in south-central Texas. The optimal solutions of this case study highlight the significance of geographic factors and resource availability on the transition towards renewable energy generation.
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•Detailed models of key components of energy systems with water-land considerations•Integrated optimization model for infrastructure planning of energy systems•Multi-objective optimization approach for energy-water-land nexus trade-off analysis•Derivation of cost power output surrogate models for renewable energy technologies
•Formulation of the multi-objective optimization problem as a multi-parametric QCQP.•Derivation of suitable affine overestimators with a guaranteed bound of suboptimality.•Solution of the resulting ...mp-QP problem with state-of-the-art solvers, thus obtaining the Pareto front explicitly.•Numerical examples highlight the capabilities of this approach.
In this note we present an approximate algorithm for the explicit calculation of the Pareto front for multi-objective optimization problems featuring convex quadratic cost functions and linear constraints based on multi-parametric programming and employing a set of suitable overestimators with tunable suboptimality. A numerical example as well as a small computational study highlight the features of the novel algorithm.
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