Carbon dioxide conversion and utilization is gaining significant attention worldwide, not only because carbon dioxide has an impact on global climate change, but also because it provides a source for potential fuels and chemicals. Methanol is an important fuel that can be obtained by the hydrogenation of carbon dioxide. In this research, the modeling of a reactor to produce methanol using carbon dioxide and hydrogen is carried out by way of an ANOVA and a central composite design. Reaction temperature, reaction pressure, H₂/CO₂ ratio, and recycling are the chosen factors, while the methanol production and the reactor volume are the studied responses. Results show that the interaction AC is common between the two responses and allows improvement of the productivity in reducing the volume. A mathematical model for methanol production and reactor volume is obtained with significant factors. A central composite design is used to optimize the process. Results show that a higher productivity... [more]

This paper presents a review of history matching and oil field development optimization techniques with a focus on optimization algorithms. History matching algorithms are reviewed as a precursor to production optimization algorithms. Techniques for history matching and production optimization are reviewed including global and local methods. Well placement, well control, and combined well placement-control optimization using both secondary and tertiary oil production techniques are considered. Secondary and tertiary recovery techniques are commonly referred to as waterflooding and enhanced oil recovery (EOR), respectively. Benchmark models for comparison of methods are summarized while other applications of methods are discussed throughout. No single optimization method is found to be universally superior. Key areas of future work are combining optimization methods and integrating multiple optimization processes. Current challenges and future research opportunities for improved model v... [more]

Process optimization is the method of choice for improving the performance of industrial processes, while also enforcing the satisfaction of safety and quality constraints.[...]

The modifier approach has been extensively explored and offers a theoretically-sound and practically-useful method to deploy real-time optimization. The recent directional-modifier adaptation algorithm offers a heuristic to tackle the modifier approach. The directional-modifier adaptation algorithm, supported by strong theoretical properties and the ease of deployment in practice, proposes a meaningful compromise between process optimality and quickly improving the quality of the estimation of the gradient of the process cost function. This paper proposes a novel view of the directional-modifier adaptation algorithm, as an approximation of the optimal trade-off between the underlying experimental design problem and the process optimization problem. It moreover suggests a minor modification in the tuning of the algorithm, so as to make it a more genuine approximation.

This paper presents an overview of the recent developments of modifier-adaptation schemes for real-time optimization of uncertain processes. These schemes have the ability to reach plant optimality upon convergence despite the presence of structural plant-model mismatch. Modifier Adaptation has its origins in the technique of Integrated System Optimization and Parameter Estimation, but differs in the definition of the modifiers and in the fact that no parameter estimation is required. This paper reviews the fundamentals of Modifier Adaptation and provides an overview of several variants and extensions. Furthermore, the paper discusses different methods for estimating the required gradients (or modifiers) from noisy measurements. We also give an overview of the application studies available in the literature. Finally, the paper briefly discusses open issues so as to promote future research in this area.

In this work, we consider the problem of daily production optimization in the upstream oil and gas domain. The objective is to find the optimal decision variables that utilize the production systems efficiently and maximize the revenue. Typically, mathematical models are used to find the optimal operation in such processes. However, such prediction models are subject to uncertainty that has been often overlooked, and the optimal solution based on nominal models can thus render the solution useless and may lead to infeasibility when implemented. To ensure robust feasibility, worst case optimization may be employed; however, the solution may be rather conservative. Alternatively, we propose the use of scenario-based optimization to reduce the conservativeness. The results of the nominal, worst case and scenario-based optimization are compared and discussed.

Changing the combustion process of a gas turbine from a constant-pressure to a pressure-increasing approximate constant-volume combustion (aCVC) is one of the most promising ways to increase the efficiency of turbines in the future. In this paper, a newly proposed method to achieve such an aCVC is considered. The so-called shockless explosion combustion (SEC) uses auto-ignition and a fuel stratification to achieve a spatially homogeneous ignition. The homogeneity of the ignition can be adjusted by the mixing of fuel and air. A proper filling profile, however, also depends on changing parameters, such as temperature, that cannot be measured in detail due to the harsh conditions inside the combustion tube. Therefore, a closed-loop control is required to obtain an adequate injection profile and to reject such unknown disturbances. For this, an optimization problem is set up and a novel formulation of a discrete extremum seeking controller is presented. By approximating the cost function w... [more]

This work presents a methodology to represent logical decisions in differential algebraic equation simulation and constrained optimization problems using a set of continuous algebraic equations. The formulations may be used when state variables trigger a change in process dynamics, and introduces a pseudo-binary decision variable, which is continuous, but should only have valid solutions at values of either zero or one within a finite time horizon. This formulation enables dynamic optimization problems with logical disjunctions to be solved by simultaneous solution methods without using methods such as mixed integer programming. Several case studies are given to illustrate the value of this methodology including nonlinear model predictive control of a chemical reactor using a surge tank with overflow to buffer disturbances in feed flow rate. Although this work contains novel methodologies for solving dynamic algebraic equation (DAE) constrained problems where the system may experience... [more]

Artificial neural networks (ANNs) are used in various applications for data-driven black-box modeling and subsequent optimization. Herein, we present an efficient method for deterministic global optimization of ANN embedded optimization problems. The proposed method is based on relaxations of algorithms using McCormick relaxations in a reduced-space [\textit{SIOPT}, 20 (2009), pp. 573-601] including the convex and concave envelopes of the nonlinear activation function of ANNs. The optimization problem is solved using our in-house global deterministic solver MAiNGO. The performance of the proposed method is shown in four optimization examples: an illustrative function, a fermentation process, a compressor plant and a chemical process optimization. The results show that computational solution time is favorable compared to the global general-purpose optimization solver BARON.