Optimal management of bio-based energy supply chains under parametric uncertainty through a data-driven decision-support framework

This paper addresses the optimal management of a multi-objective bio-based energy supply chain network subjected to multiple sources of uncertainty. The complexity to obtain an optimal solution using traditional uncertainty management methods dramatically increases with the number of uncertain factors considered. Such a complexity produces that, if tractable, the problem is solved after a large computational effort. Therefore, in this work a data-driven decision-making framework is proposed to address this issue. Such a framework exploits machine learning techniques to efficiently approximate the optimal management decisions considering a set of uncertain parameters that continuously influence the process behavior as an input. A design of computer experiments technique is used in order to combine these parameters and produce a matrix of representative information. These data are used to optimize the deterministic multi-objective bio-based energy network problem through conventional optimization methods, leading to a detailed (but elementary) map of the optimal management decisions based on the uncertain parameters. Afterwards, the detailed data-driven relations are described/identified using an Ordinary Kriging meta-model. The result exhibits a very high accuracy of the parametric meta-models for predicting the optimal decision variables in comparison with the traditional stochastic approach. Besides, and more importantly, a dramatic reduction of the computational effort required to obtain these optimal values in response to the change of the uncertain parameters is achieved. Thus the use of the proposed data-driven decision tool promotes a time-effective optimal decision making, which represents a step forward to use data-driven strategy in large-scale/complex industrial problems.Peer ReviewedPostprint (published version

Measurements, Models, Systems and Design

531 s.

Extraction and Characterization of Essential Discharge Patterns from Multisite Recordings of Spiking Ongoing Activity

Conditional sampling, in comparison with the classical constant time-bin sampling, enables to reject, at least in most cases, the common mode modulation of the spiking frequency across different spiking sources. Here we consider a simple but significant example while a more general analysis is currently in preparation: Consider two spiking neurons and let n1, n2 the number of spikes emitted in a time period T. They both follow a Poisson process with parameters λcλ1T and λcλ2T respectively, being λc a common modulation term, λ1 and λ2 the independent component of their activity. Let n1 + n2 = k and Pn1,n2 = Pn1,k−n1 the probability of observing n1 and k − n1 spikes (respectively from the first and the second neuron) in a period T. Then Pn1,k−n1 = e−λcT (λ1+λ2) (T λc) k λn 1 1 λk−n 1 2 n1!(k−n1)! Now consider the conditional probability of observing n1 and k − n1 spikes i