Data augmentation driven by optimization for membrane separation process synthesis

This paper proposes a new hybrid strategy to optimally design membrane separation problems. We formulate the problem as a Non-Linear Programming (NLP) model. A common approach to represent the physical behavior of the membrane is to discretize the system of differential equations that govern the separation process. Instead, we represent the input/output behavior of the single membrane by an artificial neural network (ANN) predictor. The ANN is trained on a dataset obtained through the MEMSIC simulator. The equation form of the trained predictor (shape and weights) is then inserted in the NLP model at the place of the discretized system of differential equations. To improve the ANN accuracy without an excessive computational burden, we propose data augmentation strategies to target the regions where densify the dataset. We compare a data augmentation strategy from the literature with a novel one that densifies the dataset around the stationary points visited by a global optimization algorithm. Our approach was validated using a relevant industrial case study: hydrogen purification. Validation by simulation is performed on the obtained solutions. The computational results show that a data augmentation smartly coupled with optimization can produce a robust and reliable design tool

Processus de séparation par membrane à l'aide de modèles de programmation mathématique basés sur l'apprentissage automatique

The main topic of my Ph.D. is the optimization of membrane separation technology. Membrane separation technology is often used to achieve gas purification and it can be used in different aspects of the industry. The membrane separation performance depends on the operating conditions and the interconnections between the selected equipment. Membranes for gas separation can be made of different materials, and each material leads to different permeability performances. When a mixture of gas enters the membrane, some components having low permeability pass through the membrane as in a tube, forming the retentate output, whereas other components with higher permeability drop through the material, forming the permeated output of the membrane. When a high level of purity is required, one separation stage is not enough, and multiple stages are needed. In this case, a problem of membrane system design has to be solved where the number of stages, the interconnections, and the operating conditions for each stage has to be chosen. The objective function to be considered is the cost of the system, ensuring a certain level of performance in terms of purity and recovery of the desired gas. Up to now, the problem has been solved using a heuristic global optimization approach, which was a combination of multi-start and a problem-tailored Monotonic Basin Hopping. The proposed method was applied to optimize and analyze several well-known and important gas separation cases. The degrees of freedom of the optimization model were increased case by case considering more parameters as decision variables and optimizing the separation process design. The obtained results were good, but since the algorithm is heuristic, there is no guarantee of finding the optimal global solution.Le sujet principal de mon doctorat est l'optimisation de la technologie de séparation membranaire. La technologie de séparation par membrane est souvent utilisée pour purifier les gaz et peut être utilisée dans différents aspects de l'industrie. Les performances de la séparation membranaire dépendent des conditions de fonctionnement et des interconnexions entre les équipements sélectionnés. Les membranes pour la séparation des gaz peuvent être fabriquées à partir de différents matériaux, et chaque matériau entraîne des performances de perméabilité différentes. Lorsqu'un mélange de gaz pénètre dans la membrane, certains composants peu perméables traversent la membrane comme dans un tube, formant le rétentat de sortie, tandis que d'autres composants plus perméables tombent à travers le matériau, formant le perméat de sortie. Lorsqu'un haut niveau de pureté est requis, une étape de séparation n'est pas suffisante et plusieurs étapes sont nécessaires. Dans ce cas, il faut résoudre le problème de la conception du système membranaire en choisissant le nombre d'étages, les interconnexions et les conditions de fonctionnement de chaque étage. La fonction objective à considérer est le coût du système, en garantissant un certain niveau de performance en termes de pureté et de récupération du gaz désiré. Jusqu'à présent, le problème a été résolu à l'aide d'une approche heuristique d'optimisation globale, qui était une combinaison de multi-démarrages et d'un saut de bassin monotone adapté au problème. La méthode proposée a été appliquée pour optimiser et analyser plusieurs cas bien connus et importants de séparation des gaz. Les degrés de liberté du modèle d'optimisation ont été augmentés au cas par cas, en considérant davantage de paramètres comme variables de décision et en optimisant la conception du processus de séparation. Les résultats obtenus sont bons, mais comme l'algorithme est heuristique, il n'y a aucune garantie de trouver la solution globale optimale

Processus de séparation par membrane à l'aide de modèles de programmation mathématique basés sur l'apprentissage automatique

The main topic of my Ph.D. is the optimization of membrane separation technology. Membrane separation technology is often used to achieve gas purification and it can be used in different aspects of the industry. The membrane separation performance depends on the operating conditions and the interconnections between the selected equipment. Membranes for gas separation can be made of different materials, and each material leads to different permeability performances. When a mixture of gas enters the membrane, some components having low permeability pass through the membrane as in a tube, forming the retentate output, whereas other components with higher permeability drop through the material, forming the permeated output of the membrane. When a high level of purity is required, one separation stage is not enough, and multiple stages are needed. In this case, a problem of membrane system design has to be solved where the number of stages, the interconnections, and the operating conditions for each stage has to be chosen. The objective function to be considered is the cost of the system, ensuring a certain level of performance in terms of purity and recovery of the desired gas. Up to now, the problem has been solved using a heuristic global optimization approach, which was a combination of multi-start and a problem-tailored Monotonic Basin Hopping. The proposed method was applied to optimize and analyze several well-known and important gas separation cases. The degrees of freedom of the optimization model were increased case by case considering more parameters as decision variables and optimizing the separation process design. The obtained results were good, but since the algorithm is heuristic, there is no guarantee of finding the optimal global solution.Le sujet principal de mon doctorat est l'optimisation de la technologie de séparation membranaire. La technologie de séparation par membrane est souvent utilisée pour purifier les gaz et peut être utilisée dans différents aspects de l'industrie. Les performances de la séparation membranaire dépendent des conditions de fonctionnement et des interconnexions entre les équipements sélectionnés. Les membranes pour la séparation des gaz peuvent être fabriquées à partir de différents matériaux, et chaque matériau entraîne des performances de perméabilité différentes. Lorsqu'un mélange de gaz pénètre dans la membrane, certains composants peu perméables traversent la membrane comme dans un tube, formant le rétentat de sortie, tandis que d'autres composants plus perméables tombent à travers le matériau, formant le perméat de sortie. Lorsqu'un haut niveau de pureté est requis, une étape de séparation n'est pas suffisante et plusieurs étapes sont nécessaires. Dans ce cas, il faut résoudre le problème de la conception du système membranaire en choisissant le nombre d'étages, les interconnexions et les conditions de fonctionnement de chaque étage. La fonction objective à considérer est le coût du système, en garantissant un certain niveau de performance en termes de pureté et de récupération du gaz désiré. Jusqu'à présent, le problème a été résolu à l'aide d'une approche heuristique d'optimisation globale, qui était une combinaison de multi-démarrages et d'un saut de bassin monotone adapté au problème. La méthode proposée a été appliquée pour optimiser et analyser plusieurs cas bien connus et importants de séparation des gaz. Les degrés de liberté du modèle d'optimisation ont été augmentés au cas par cas, en considérant davantage de paramètres comme variables de décision et en optimisant la conception du processus de séparation. Les résultats obtenus sont bons, mais comme l'algorithme est heuristique, il n'y a aucune garantie de trouver la solution globale optimale

Processus de séparation par membrane à l'aide de modèles de programmation mathématique basés sur l'apprentissage automatique

Le sujet principal de mon doctorat est l'optimisation de la technologie de séparation membranaire. La technologie de séparation par membrane est souvent utilisée pour purifier les gaz et peut être utilisée dans différents aspects de l'industrie. Les performances de la séparation membranaire dépendent des conditions de fonctionnement et des interconnexions entre les équipements sélectionnés. Les membranes pour la séparation des gaz peuvent être fabriquées à partir de différents matériaux, et chaque matériau entraîne des performances de perméabilité différentes. Lorsqu'un mélange de gaz pénètre dans la membrane, certains composants peu perméables traversent la membrane comme dans un tube, formant le rétentat de sortie, tandis que d'autres composants plus perméables tombent à travers le matériau, formant le perméat de sortie. Lorsqu'un haut niveau de pureté est requis, une étape de séparation n'est pas suffisante et plusieurs étapes sont nécessaires. Dans ce cas, il faut résoudre le problème de la conception du système membranaire en choisissant le nombre d'étages, les interconnexions et les conditions de fonctionnement de chaque étage. La fonction objective à considérer est le coût du système, en garantissant un certain niveau de performance en termes de pureté et de récupération du gaz désiré. Jusqu'à présent, le problème a été résolu à l'aide d'une approche heuristique d'optimisation globale, qui était une combinaison de multi-démarrages et d'un saut de bassin monotone adapté au problème. La méthode proposée a été appliquée pour optimiser et analyser plusieurs cas bien connus et importants de séparation des gaz. Les degrés de liberté du modèle d'optimisation ont été augmentés au cas par cas, en considérant davantage de paramètres comme variables de décision et en optimisant la conception du processus de séparation. Les résultats obtenus sont bons, mais comme l'algorithme est heuristique, il n'y a aucune garantie de trouver la solution globale optimale.The main topic of my Ph.D. is the optimization of membrane separation technology. Membrane separation technology is often used to achieve gas purification and it can be used in different aspects of the industry. The membrane separation performance depends on the operating conditions and the interconnections between the selected equipment. Membranes for gas separation can be made of different materials, and each material leads to different permeability performances. When a mixture of gas enters the membrane, some components having low permeability pass through the membrane as in a tube, forming the retentate output, whereas other components with higher permeability drop through the material, forming the permeated output of the membrane. When a high level of purity is required, one separation stage is not enough, and multiple stages are needed. In this case, a problem of membrane system design has to be solved where the number of stages, the interconnections, and the operating conditions for each stage has to be chosen. The objective function to be considered is the cost of the system, ensuring a certain level of performance in terms of purity and recovery of the desired gas. Up to now, the problem has been solved using a heuristic global optimization approach, which was a combination of multi-start and a problem-tailored Monotonic Basin Hopping. The proposed method was applied to optimize and analyze several well-known and important gas separation cases. The degrees of freedom of the optimization model were increased case by case considering more parameters as decision variables and optimizing the separation process design. The obtained results were good, but since the algorithm is heuristic, there is no guarantee of finding the optimal global solution

CO2 / H2 separation by multistage H2 selective and CO2 selective membranes: a process synthesis study

International audienc

Membrane separation processes are a rapidly spreading technology, since they can result in energyefficient, small sized and environmental friendly processes. Their optimal design can be modelled asa MINLP problem. The resulting problem can be reduced to a small subset of NLP subproblems,under some restrictive hypotheses, and using symmetry reduction techniques. Nevertheless, each ofthese sub-problems remains challenging due to the large number of non-convex equality constraintsthat represent the physical behaviour of each membrane in the system. The aim of this phase of ourwork is to compare the original full-equation model with a machine learning hybridated model (used torepresent the single membrane behaviour) in terms of quality of solutions and computational times

International audienc

MOSES: A New Approach to Integrate Interactome Topology and Functional Features for Disease Gene Prediction

Disease gene prediction is to date one of the main computational challenges of precision medicine. It is still uncertain if disease genes have unique functional properties that distinguish them from other non-disease genes or, from a network perspective, if they are located randomly in the interactome or show specific patterns in the network topology. In this study, we propose a new method for disease gene prediction based on the use of biological knowledge-bases (gene-disease associations, genes functional annotations, etc.) and interactome network topology. The proposed algorithm called MOSES is based on the definition of two somewhat opposing sets of genes both disease-specific from different perspectives: warm seeds (i.e., disease genes obtained from databases) and cold seeds (genes far from the disease genes on the interactome and not involved in their biological functions). The application of MOSES to a set of 40 diseases showed that the suggested putative disease genes are significantly enriched in their reference disease. Reassuringly, known and predicted disease genes together, tend to form a connected network module on the human interactome, mitigating the scattered distribution of disease genes which is probably due to both the paucity of disease-gene associations and the incompleteness of the interactome

MOSES: A New Approach to Integrate Interactome Topology and Functional Features for Disease Gene Prediction

Disease gene prediction is to date one of the main computational challenges of precision medicine. It is still uncertain if disease genes have unique functional properties that distinguish them from other non-disease genes or, from a network perspective, if they are located randomly in the interactome or show specific patterns in the network topology. In this study, we propose a new method for disease gene prediction based on the use of biological knowledge-bases (gene-disease associations, genes functional annotations, etc.) and interactome network topology. The proposed algorithm called MOSES is based on the definition of two somewhat opposing sets of genes both disease-specific from different perspectives: warm seeds (i.e., disease genes obtained from databases) and cold seeds (genes far from the disease genes on the interactome and not involved in their biological functions). The application of MOSES to a set of 40 diseases showed that the suggested putative disease genes are significantly enriched in their reference disease. Reassuringly, known and predicted disease genes together, tend to form a connected network module on the human interactome, mitigating the scattered distribution of disease genes which is probably due to both the paucity of disease-gene associations and the incompleteness of the interactome.</jats:p

Moses: A new approach to integrate interactome topology and functional features for disease gene prediction

Disease gene prediction is to date one of the main computational challenges of precision medicine. It is still uncertain if disease genes have unique functional properties that distinguish them from other non-disease genes or, from a network perspective, if they are located randomly in the interactome or show specific patterns in the network topology. In this study, we propose a new method for disease gene prediction based on the use of biological knowledge-bases (gene-disease associations, genes functional annotations, etc.) and interactome network topology. The proposed algorithm called MOSES is based on the definition of two somewhat opposing sets of genes both disease-specific from different perspectives: warm seeds (i.e., disease genes obtained from databases) and cold seeds (genes far from the disease genes on the interactome and not involved in their biological functions). The application of MOSES to a set of 40 diseases showed that the suggested putative disease genes are significantly enriched in their reference disease. Reassuringly, known and predicted disease genes together, tend to form a connected network module on the human interactome, mitigating the scattered distribution of disease genes which is probably due to both the paucity of disease-gene associations and the incompleteness of the interactome