From conceptual design to process design optimization: a review on flowsheet synthesis

International audienceThis paper presents the authors’ perspectives on some of the open questions and opportunities in Process Systems Engineering (PSE) focusing on process synthesis. A general overview of process synthesis is given, and the difference between Conceptual Design (CD) and Process Design (PD) is presented using an original ternary diagram. Then, a bibliometric analysis is performed to place major research team activities in the latter. An analysis of ongoing work is conducted and some perspectives are provided based on the analysis. This analysis includes symbolic knowledge representation concepts and inference techniques, i.e., ontology, that is believed to become useful in the future. Future research challenges that process synthesis will have to face, such as biomass transformation, shale production, response to spaceflight demand, modular plant design, and intermittent production of energy, are also discussed

A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics

The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at an accelerated pace. Because of that, in spite of the fact that there are a lot of surveys and reviews in the field, they quickly become dated. Thus, it is of importance to keep pace with the current developments. In this review, we first consider a possible classification of bio-inspired multiparameter optimization methods because papers dedicated to that area are relatively scarce and often contradictory. We proceed by describing in some detail some more prominent approaches, as well as those most recently published. Finally, we consider the use of biomimetic algorithms in two related wide fields, namely microelectronics (including circuit design optimization) and nanophotonics (including inverse design of structures such as photonic crystals, nanoplasmonic configurations and metamaterials). We attempted to keep this broad survey self-contained so it can be of use not only to scholars in the related fields, but also to all those interested in the latest developments in this attractive area

Genetic Algorithms in Supply Chain Scheduling of Ready-Mixed Concrete

The coordination of just-in-time production and transportation in a network of partially independent facilities to guarantee timely delivery to distributed customers is one of the most challenging aspects of supply chain management. From the theoretical perspective, the timely production/distribution can be viewed as a hybrid combination of planning, scheduling and routing problem, each notoriously affected by nearly prohibitive combinatorial complexity. From a practical viewpoint, the problem calls for a trade-off between risks and profits. This paper focuses on the ready-made concrete delivery: in addition to the mentioned complexity, strict time-constraints forbid both earliness and lateness of the supply. After developing a detailed model of the considered problem, we propose a novel meta-heuristic approach based on a hybrid genetic algorithm combined with constructive heuristics. A detailed case study derived from industrial data is used to illustrate the potential of the proposed approach

Advances in Evolutionary Algorithms

With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field

Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors

Music in Evolution and Evolution in Music

Music in Evolution and Evolution in Music by Steven Jan is a comprehensive account of the relationships between evolutionary theory and music. Examining the ‘evolutionary algorithm’ that drives biological and musical-cultural evolution, the book provides a distinctive commentary on how musicality and music can shed light on our understanding of Darwin’s famous theory, and vice-versa. Comprised of seven chapters, with several musical examples, figures and definitions of terms, this original and accessible book is a valuable resource for anyone interested in the relationships between music and evolutionary thought. Jan guides the reader through key evolutionary ideas and the development of human musicality, before exploring cultural evolution, evolutionary ideas in musical scholarship, animal vocalisations, music generated through technology, and the nature of consciousness as an evolutionary phenomenon. A unique examination of how evolutionary thought intersects with music, Music in Evolution and Evolution in Music is essential to our understanding of how and why music arose in our species and why it is such a significant presence in our lives

Experimental investigation and modelling of the heating value and elemental composition of biomass through artificial intelligence

Abstract: Knowledge advancement in artificial intelligence and blockchain technologies provides new potential predictive reliability for biomass energy value chain. However, for the prediction approach against experimental methodology, the prediction accuracy is expected to be high in order to develop a high fidelity and robust software which can serve as a tool in the decision making process. The global standards related to classification methods and energetic properties of biomass are still evolving given different observation and results which have been reported in the literature. Apart from these, there is a need for a holistic understanding of the effect of particle sizes and geospatial factors on the physicochemical properties of biomass to increase the uptake of bioenergy. Therefore, this research carried out an experimental investigation of some selected bioresources and also develops high-fidelity models built on artificial intelligence capability to accurately classify the biomass feedstocks, predict the main elemental composition (Carbon, Hydrogen, and Oxygen) on dry basis and the Heating value in (MJ/kg) of biomass...Ph.D. (Mechanical Engineering Science

Validación de las redes neuronales artificiales como metodología para la asignación donante-receptor en el trasplante hepático

1. Introducción o motivación de la tesis. El trasplante hepático constituye la mejor opción terapéutica para un gran número de patologías hepáticas en fase terminal. Desafortunadamente, existe un disbalance entre el número de candidatos y el número de donantes disponibles, lo que conlleva a muertes y exclusiones en lista de espera. En los últimos años se han realizado numerosos esfuerzos para incrementar el pool de donantes, así como para optimizar la priorización en lista de los posibles receptores. Entre ellos, destacan la utilización de los denominados “donantes con criterios extendidos” (ECD, extended criteria donors) y la adopción de un sistema de priorización mediante un score basado en la gravedad del candidato (MELD, Mayo Model for End Stage Liver Disease). La asignación donante-receptor es un factor determinante en los resultados del trasplante hepático, para lo cual se han propuesto múltiples “scores” en la literatura. Sin embargo, ninguno de ellos se considera óptimo para realizar este emparejamiento. En 2014, nuestro grupo publicó la utilidad de las redes neuronales artificiales (RNA) como una herramienta óptima para el matching donante-receptor en el trasplante hepático. Para ello se realizó un estudio multicéntrico a nivel nacional, en el que se demostró la superioridad de este modelo para predecir la supervivencia post-trasplante. El objetivo de nuestro estudio es analizar si las redes neuronales tienen un comportamiento similar al demostrado en España en un sistema de salud diferente, y si son una herramienta superior a los modelos actuales utilizados para el matching donante-receptor. 2. Contenido de la investigación. Se recogieron 822 pares donante-receptor (D-R) de trasplantes hepáticos realizados de forma consecutiva en el hospital King’s College de Londres durante los años 2002 a 2010, teniendo en cuenta variables del donante, del receptor y del trasplante. Para cada par, se calcularon dos probabilidades: la probabilidad de supervivencia (CCR) y la probabilidad de pérdida del injerto (MS) a los 3 meses del trasplante. Para ello se construyeron dos modelos de redes neuronales artificiales diferentes y no complementarios: el modelo de aceptación y el modelo de rechazo. Se construyeron varios modelos: 1) Entrenamiento y generalización con los pares D-R del hospital británico (a 3 y a 12 meses post-trasplante) , 2) Entrenamiento con pares D-R españoles y generalización con los británicos y 3) Modelo combinado: entrena y generaliza con pares españoles y británicos. Además, para ayudar en la toma de decisiones según los resultados obtenidos por la red neuronal, se construyó un sistema basado en reglas. Los modelos diseñados para el hospital King’s College demostraron una excelente capacidad de predicción para ambos: 3 meses (CCR-AUC=0,9375; MS-AUC=0,9374) y 12 meses (CCR-AUC=0,7833; MS-AUC=0,8153), casi un 15% superior a la mejor capacidad de predicción obtenida por otros scores como MELD o BAR (Balance of Risk). Además, estos resultados mejoran los publicados previamente en el modelo multicéntrico español. Sin embargo, esta capacidad de predicción no es tan buena cuando el modelo entrena y generaliza con pares D-R procedentes de sistemas de salud diferentes, ni tampoco en el modelo combinado. 3.Conclusiones. 1. El empleo de Redes Neuronales Artificiales para la Asignación Donante-Receptor en el Trasplante Hepático ha demostrado excelentes capacidades de predicción de Supervivencia y No Supervivencia del injerto, al ser validadas en un sistema de salud distinto de otro país, por lo tanto la metodología de la Inteligencia Artificial ha quedado claramente validada como herramienta óptima para el “matching D-R”. 2. Nuestros resultados apoyan que los distintos equipos de Trasplante Hepático consideren las Redes Neuronales Artificiales como el método más exhaustivo y objetivo descrito hasta la fecha para el manejo de la lista de espera del Trasplante Hepático, evitando criterios subjetivos y arbitrarios y maximizando los principios de equidad, utilidad y eficiencia. 3. Nuestro modelo de validación, es decir, la RNA generada con pares D-R del Hospital King’s College de Londres ha logrado la máxima capacidad de predicción, superando el resto de modelos y apoyando el hecho de que cada RNA debe ser entrenada, testada y optimizada para un propósito específico, en una única población. Así, cada programa de TH debería disponer de su propio modelo construido con sus propios datos, para apoyar la decisión del “matching D-R”. 4. El modelo de Asignación D-R generado por las RNAs combina lo mejor del sistema MELD con el Beneficio de Supervivencia Global, usando para ello un sistema basado en reglas, maximizando la utilidad de los injertos disponibles. Esto los convierte en sistemas complementarios para un mismo fin, en lugar de considerarlos competitivos

SciTech News Volume 70, No. 2 (2016)

Table of Contents: Columns and Reports From the Editor 3 Division News Science-Technology Division 4 New Members 6 Chemistry Division 7 New Members11 Engineering Division 12 Aerospace Section of the Engineering Division 17 Reviews Sci-Tech Book News Reviews 1