Using artificial neural networks to accelerate flowsheet optimization for downstream process development

An optimal purification process for biopharmaceutical products is important to meet strict safety regulations, and for economic benefits. To find the global optimum, it is desirable to screen the overall design space. Advanced model-based approaches enable to screen a broad range of the design-space, in contrast to traditional statistical or...

Machine Learning-Based Surrogate Modeling for Urban Water Networks: Review and Future Research Directions

Surrogate models replace computationally expensive simulations of physically-based models to obtain accurate results at a fraction of the time. These surrogate models, also known as metamodels, have been employed for analysis, control, and optimization of water distribution and urban drainage systems. With the advent of machine learning (ML),...

MPC Based Centralized Voltage and Reactive Power Control for Active Distribution Networks

The paper presents an approach for online centralized control in active distribution networks. It combines a proportional integral (PI) control unit with a corrective control unit (CCU), based on the principle of Model Predictive Control (MPC). The proposed controller is designed to accommodate the increasing penetration of distributed...

Artificial Intelligence techniques in design optimization of variable stiffness cylindrical shells

The main driver of aerospace structures design is the increase in performances of currently in use components. The behavior of structures is investigated by means of highly accurate finite elements (FE) analysis. The problem related to this kind of simulations is the high computational time required to obtain the structural response associated...

Consolidated Deep Actor Critic Networks (DRAFT)

The works [Volodymyr et al. Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602, 2013.] and [Volodymyr et al. Human-level control through deep reinforcement learning. Nature, 518(7540):529–533, 2015.] have demonstrated the power of combining deep neural networks with Watkins Q learning. They introduce deep Q networks ...

Real-time Environmental Feedback at the Early Design Stages

It has been argued that traditional building simulation methods can be a slow process, which often fails to integrate into the decision making process of non-technical designers, such as architects, at the early design stages. Furthermore, studies have shown that predicted energy consumption of buildings during design is often lower than...

Computational Intelligence in Rainfall-Runoff Modeling

The transformation from precipitation over a river basin to river streamflow is the result of many interacting processes which manifest themselves at various scales of time and space. The resulting complexity of hydrological systems, and the difficulty to properly and quantitatively express the information that is available about them, determine...

Multiobjective training of artificial neural networks for rainfall-runoff modeling

This paper presents results on the application of various optimization algorithms for the training of artificial neural network rainfall-runoff models. Multilayered feed-forward networks for forecasting discharge from two mesoscale catchments in different climatic regions have been developed for this purpose. The performances of the...

Clinical study for classification of benign, dysplastic, and malignant oral lesions using autofluorescence spectroscopy