Resource effiency indicator-based decision support for the operation of batch and mixed batch-continuous processing plants

Steigende Konzentrationen von Treibhausgasen in der Atmosphäre sind der Grund für den globalen Klimawandel. Da die chemische Industrie wesentlich zu den Treibhausgasemissionen beiträgt, schaffen politische Entscheidungsträger Anreize und Gesetze, um die Industrie zu einer nachhaltigeren Produktion zu bewegen. In dieser Arbeit wird ein Rahmen zur Definition und Nutzung von Echtzeit-Ressourcene zienzindikatoren (REI) entwickelt, um die Ressourceneffizienz industrieller Produktionsprozesse kontinuierlich zu überwachen und zu optimieren. Die Ressourceneffizienz ist eine mehrdimensionale Größe, die in Relation zur Wirtschaftlichkeit bewertet werden kann. Der Fokus der Arbeit liegt dabei auf Batch-Prozessen und Prozessen, die diskontinuierliche und kontinuierliche Teilprozesse kombinieren. Diese stellen eine Herausforderung für die korrekte Erfassung relevanter Prozessgrößen und die anschlie ende Analyse dar. Das vorgeschlagene Propagationskonzept ermöglicht es, den Gesamtwirkungsgrad der Anlage auf Basis der Leistung ihrer Komponenten zu berechnen. Die daraus resultierenden REIs spiegeln die technische Leistung der Anlage wieder und werden zur Optimierung der gesamten Ressourceneffizienz eines Anwendungsbeispiels verwendet. Die Optimierung der Ressourceneeffizienz stellt ein mehrdimensionales Optimierungsproblem dar, bei dem die Pareto-optimalen Betriebspunkte die möglichen Kompromisse zwischen konkurrierenden Interessen angeben. Die Auswahl eines gewünschten Betriebspunktes aus der Paretomenge ist nicht trivial und kann sich ändernden Präferenzen folgen. Daher befasst sich der zweite Teil der Arbeit mit der Synthese eines effizienten und effektiven Entscheidungsunterstützungssystems (Decision Support System, DSS) zur Auswahl eines Betriebspunktes mit dem gewünschten Leistungsprofil. Die Methodik wird auf ein Beispiel angewendet und durch eine experimentelle Usability-Studie validiert. Damit leistet diese Arbeit einen Beitrag zur Optimierung der Ressourceneffizienz in der Prozessindustrie durch die Identifikation von ressourcenoptimalen Betriebszuständen. Die ganzheitliche Betrachtung der Ressourceneffizienz in Batchprozessen stellt eine wichtige Erweiterung der industriellen Praxis dar, die sich derzeit in der Regel auf eine Energieeffizienzanalyse nach ISO50001 beschränkt.Increasing concentrations of greenhouse gases (GHG) in the atmosphere are the reason for global climate change. Since the chemical industry is a signficant contributor to the GHG emissions, policy makers are creating incentives and legislation to steer the industry towards a more sustainable production. This thesis proposes a framework to defie and utilize real-time resource effiency indicators (REI) to constantly monitor and optimize the resource effiency of industrial production processes. Resource effiency is a multidimensional entity that can be evaluated in relation to the economic performance. The focus of the thesis is on batch- and hybrid - coupled batch and continuously operated -{ processes that introduce further challenges for the correct recording of relevant process variables and the subsequent analysis. The proposed propagation concept makes it possible to calculate the overall effiency of the plant based on the performance of its components. The resulting REIs reflect the technical performance of the plant and are used to optimize the overall resource effiency of an application case. Optimizing the resource effiency of a process poses a multi-dimensional optimization problem, where the Pareto optimal operating points reflect the potential trade-offs between competing interests. The selection of a desired operational point among the optimal set is not trivial and may be subject to changing preferences. Thus, the second part of the thesis addresses the synthesis of an effcient and effective decision support system (DSS) to select an operating point with the desired performance profile. The methodology is applied and validated by an experimental usability-study. In summary, the thesis contributes to the optimization of resource effiency in the process industry by identifying resource-optimal operating conditions. The holistic consideration of resource effiency in batch processes represents an important extension of industrial practice, which is up to now usually limited to an energy effiency analysis according to ISO50001

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Decision support for ranking Pareto optimal process designs under uncertain market conditions

Considering the uncertainty of economic conditions, multi-objective optimisation can be favoured to single-objective optimisation for process design. However, from the Pareto sets generated by multi- objective optimisation it is not obvious to identify the best one, given that each solution is optimal with regard to the selected objectives. A method taking into account the economic parameters uncertainty to support decision making based on the Pareto-optimal solutions is proposed. It uses a Monte-Carlo simulation to define the probability of each of the Pareto optimal configuration to be in the list of the best configurations from the economical point of view. For a given economic context defined the most probable best configurations are identified. The proposed method is applied to two cases: the CO2 capture in power plants and synthetic natural gas production from biomass resources. The results allow to identify the most attractive system designs and give recommendations for the process engineers

Artificial cognitive architecture with self-learning and self-optimization capabilities. Case studies in micromachining processes

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de lectura : 22-09-201

Artificial Intelligence for Drug Discovery: Are We There Yet?

Drug discovery is adapting to novel technologies such as data science, informatics, and artificial intelligence (AI) to accelerate effective treatment development while reducing costs and animal experiments. AI is transforming drug discovery, as indicated by increasing interest from investors, industrial and academic scientists, and legislators. Successful drug discovery requires optimizing properties related to pharmacodynamics, pharmacokinetics, and clinical outcomes. This review discusses the use of AI in the three pillars of drug discovery: diseases, targets, and therapeutic modalities, with a focus on small molecule drugs. AI technologies, such as generative chemistry, machine learning, and multi-property optimization, have enabled several compounds to enter clinical trials. The scientific community must carefully vet known information to address the reproducibility crisis. The full potential of AI in drug discovery can only be realized with sufficient ground truth and appropriate human intervention at later pipeline stages.Comment: 30 pages, 4 figures, 184 reference

Multicriteria Optimization Techniques for Understanding the Case Mix Landscape of a Hospital

Various medical and surgical units operate in a typical hospital and to treat their patients these units compete for infrastructure like operating rooms (OR) and ward beds. How that competition is regulated affects the capacity and output of a hospital. This article considers the impact of treating different patient case mix (PCM) in a hospital. As each case mix has an economic consequence and a unique profile of hospital resource usage, this consideration is important. To better understand the case mix landscape and to identify those which are optimal from a capacity utilisation perspective, an improved multicriteria optimization (MCO) approach is proposed. As there are many patient types in a typical hospital, the task of generating an archive of non-dominated (i.e., Pareto optimal) case mix is computationally challenging. To generate a better archive, an improved parallelised epsilon constraint method (ECM) is introduced. Our parallel random corrective approach is significantly faster than prior methods and is not restricted to evaluating points on a structured uniform mesh. As such we can generate more solutions. The application of KD-Trees is another new contribution. We use them to perform proximity testing and to store the high dimensional Pareto frontier (PF). For generating, viewing, navigating, and querying an archive, the development of a suitable decision support tool (DST) is proposed and demonstrated.Comment: 38 pages, 17 figures, 11 table

Space mission risk, sustainability and supply chain: review, multi-objective optimization model and practical approach

This paper investigates the convergence of risk, sustainability, and supply chain in space missions, including a review of fundamental concepts, the introduction of a multi-objective conceptual optimization model, and the presentation of a practical approach. Risks associated with space missions include technical, human, launch, space environment, mission design, budgetary, and political risks. Sustainability considerations must be incorporated into mission planning and execution to ensure the long-term viability of space exploration. The study emphasizes the importance of considering environmental sustainability, resource use, ethical concerns, long-term planning, international collaboration, and public outreach in space missions. It emphasizes the significance of reducing negative environmental consequences, increasing resource use efficiency, and making responsible and ethical actions. The paper offers a multi-objective optimization conceptual model that may be used to evaluate and choose sustainable space mission tactics. This approach considers a variety of elements, including environmental effects, resource utilization, mission cost, and advantages for society. It provides a systematic decision-making approach that examines trade-offs between different criteria and identifies optimal conceptual model solutions that balance risk, sustainability, and supply chain objectives. A practical approach is also offered to demonstrate the use of the multi-criteria optimization conceptual model in a space mission scenario. The practical approach demonstrates how the model can aid in the development of mission strategies that minimize risks, maximize resource consumption, and fit with sustainability goals. Overall, this paper delivers a multi-criteria optimization conceptual model and provides a space mission planning practical approach, as well as an overview of the interaction between risk, sustainability, and supply chain in space mission organization, planning, and execution.This research was partially supported by the AGH University of Science and Technology, Kraków, Poland (16.16.200.396) and the financial aid of the Polish Ministry of Science and Higher Education (MNISW) grants (N N519 405934; 6459/B/T02/2011/40) and the Polish National Science Centre (NCN) research grant (DEC-2013/11/B/ST8/04458). Moreover, I appreciate the support of the Spanish Ministry of Science, Innovation, and Universities (RED2018-102642-T; RED2022-134703-T; PID2019-111100RB-C22/AEI/10.13039/501100011033). Additionally, I acknowledge the support from the Public University of Navarra, Pamplona, Spain and the University of California at Berkeley, USA. The research was also partially supported by the European Union Horizon 2020 research and innovation program under Marie-Skłodowska Curie, No: 101034285