Regional Integration and Security: A Comparative Perspective of the European Union and North America

The attacks of September 11, 2001 in Washington (9/11), March 11, 2004 in Madrid (11-M) and July 7 and 21 in London (7-J and 21-J) have turned security into the central issue on international and regional agendas in North America and Europe, now spreading to other regions of the world. As a result of the terrorist attacks, security has developed into an important element of integration by becoming a catalyst for agreements oriented to building security communities. The most complete representation of the construction of a North American bloc can be seen in smart border agreements and in the Security and Prosperity Partnership of North America. Undoubtedly, the security component of North America as a region is increasing and the framework for trilateral convergence exists. In the EU case, the concern about safety was clearly the main basis of accords with a view to the approval of the European Constitution, and it will also strengthen the integration process. Today, consolidating the EU is a matter of security, so Europe is securitizing its agenda.Los atentados del 11 de septiembre de 2001 en Washington, del 11 de marzo de 2004 en Madrid y del 7 y 21 de julio en Londres han convertido a la seguridad en un asunto central en las agendas nacionales e internacionales de Norteamérica y Europa, lo cual se está extendiendo hacia otras regiones del mundo. Como resultado de los atentados terroristas, la seguridad se ha tornado en un elemento importante de la integración, al transformarse en un catalizador de los acuerdos orientados a edificar comunidades de seguridad. La representación más completa de la construcción del bloque norteamericano se puede observar en los acuerdos de fronteras inteligentes y en la creación de la Alianza para la Seguridad y la Prosperidad de América del Norte. Sin duda, el componente de la seguridad de América del Norte como región se incrementa y da lugar a un marco para la convergencia trilateral. En el caso de la Unión Europea , la preocupación respecto a la seguridad fue claramente la base principal de los acuerdos con miras a la aprobación de la Constitución europea y también fortalecerá el proceso de integración. Actualmente, la consolidación de la Unión Europea es un asunto de seguridad; es por ello que Europa está securitizando su agenda

Expert-guided Bayesian Optimisation for Human-in-the-loop Experimental Design of Known Systems

Domain experts often possess valuable physical insights that are overlooked in fully automated decision-making processes such as Bayesian optimisation. In this article we apply high-throughput (batch) Bayesian optimisation alongside anthropological decision theory to enable domain experts to influence the selection of optimal experiments. Our methodology exploits the hypothesis that humans are better at making discrete choices than continuous ones and enables experts to influence critical early decisions. At each iteration we solve an augmented multi-objective optimisation problem across a number of alternate solutions, maximising both the sum of their utility function values and the determinant of their covariance matrix, equivalent to their total variability. By taking the solution at the knee point of the Pareto front, we return a set of alternate solutions at each iteration that have both high utility values and are reasonably distinct, from which the expert selects one for evaluation. We demonstrate that even in the case of an uninformed practitioner, our algorithm recovers the regret of standard Bayesian optimisation.Comment: NeurIPS 2023 Workshop on Adaptive Experimental Design and Active Learning in the Real World. Main text: 6 page

Automated structure detection for distributed process optimization

The design and control of large-scale engineering systems, consisting of a number of interacting subsystems, is a heavily researched topic with relevance both for industry and academia. This paper presents two methodologies for optimal model-based decomposition, where an optimization problem is decomposed into several smaller sub-problems and subsequently solved by augmented Lagrangian decomposition methods. Large-scale and highly nonlinear problems commonly arise in process optimization, and could greatly benefit from these approaches, as they reduce the storage requirements and computational costs for global optimization. The strategy presented translates the problem into a constraint graph. The first approach uses a heuristic community detection algorithm to identify highly connected clusters in the optimization problem graph representation. The second approach uses a multilevel graph bisection algorithm to find the optimal partition, given a desired number of sub-problems. The partitioned graphs are translated back into decomposed sets of sub-problems with a minimal number of coupling constraints. Results show both of these methods can be used as efficient frameworks to decompose optimization problems in linear time, in comparison to traditional methods which require polynomial time.Author E. A. del Rio-Chanona would like to acknowledge CONACyT scholarship No. 522530 for funding this project. Author F. Fiorelli gratefully acknowledges the support from his family. The authors would also 27 like to thank Dr Bart Hallmark, University of Cambridge, for suggesting to employ as a demonstration the chemical system in Example 7.This is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.compchemeng.2016.03.01

Safe real-time optimization using multi-fidelity guassian processes

This paper proposes a new class of real-time optimization schemes to overcome system-model mismatch of uncertain processes. This work's novelty lies in integrating derivative-free optimization schemes and multi-fidelity Gaussian processes within a Bayesian optimization framework. The proposed scheme uses two Gaussian processes for the stochastic system, one emulates the (known) process model, and another, the true system through measurements. In this way, low fidelity samples can be obtained via a model, while high fidelity samples are obtained through measurements of the system. This framework captures the system's behavior in a non-parametric fashion while driving exploration through acquisition functions. The benefit of using a Gaussian process to represent the system is the ability to perform uncertainty quantification in real-time and allow for chance constraints to be satisfied with high confidence. This results in a practical approach that is illustrated in numerical case studies, including a semi-batch photobioreactor optimization problem

Simultaneous Process Design and Control Optimization using Reinforcement Learning

With the ever-increasing numbers in population and quality in healthcare, it is inevitable for the demand of energy and natural resources to rise. Therefore, it is important to design highly efficient and sustainable chemical processes in the pursuit of sustainability. The performance of a chemical plant is highly affected by its design and control. A design cannot be evaluated without its controls and vice versa. To optimally address design and control simultaneously, one must formulate a bi-level mixed-integer nonlinear program with a dynamic optimization problem as the inner problem; this, is intractable. However, by computing an optimal policy using reinforcement learning, a controller with close-form expression can be found and embedded into the mathematical program. In this work, an approach using a policy gradient method along with mathematical programming to solve the problem simultaneously is proposed. The approach was tested in two case studies and the performance of the controller was evaluated. It was shown that the proposed approach outperforms current state-of-the-art control strategies. This opens a whole new range of possibilities to address the simultaneous design and control of engineering systems

ARRTOC: Adversarially Robust Real-Time Optimization and Control

Real-Time Optimization (RTO) plays a crucial role in the process operation hierarchy by determining optimal set-points for the lower-level controllers. However, these optimal set-points can become inoperable due to implementation errors, such as disturbances and noise, at the control layers. To address this challenge, in this paper, we present the Adversarially Robust Real-Time Optimization and Control (ARRTOC) algorithm. ARRTOC draws inspiration from adversarial machine learning, offering an online constrained Adversarially Robust Optimization (ARO) solution applied to the RTO layer. This approach identifies set-points that are both optimal and inherently robust to control layer perturbations. By integrating controller design with RTO, ARRTOC enhances overall system performance and robustness. Importantly, ARRTOC maintains versatility through a loose coupling between the RTO and control layers, ensuring compatibility with various controller architectures and RTO algorithms. To validate our claims, we present three case studies: an illustrative example, a bioreactor case study, and a multi-loop evaporator process. Our results demonstrate the effectiveness of ARRTOC in achieving the delicate balance between optimality and operability in RTO and control