A methodology for direct exploitation of available information in the online model-based redesign of experiments

Online model-based design of experiments techniques were proposed to exploit the progressive increase of the information resulting from the running experiment, but they currently exhibit some limitations: the redesign time points are chosen “a-priori” and the first design may be heavily affected by the initial parametric mismatch. In order to face such issues an information driven redesign optimisation (IDRO) strategy is here proposed: a robust approach is adopted and a new design criterion based on the maximisation of a target profile of dynamic information is introduced. The methodology allows determining when to redesign the experiment in an automatic way, thus guaranteeing that an acceptable increase in the information content has been achieved before proceeding with the intermediate estimation of the parameters and the subsequent redesign of the experiment. The effectiveness of the new experiment design technique is demonstrated through two simulated case studies

A model of chlorophyll fluorescence in microalgae integrating photoproduction, photoinhibition and photoregulation

This paper presents a mathematical model capable of quantitative prediction of the state of the photosynthetic apparatus of microalgae in terms of their open, closed and damaged reaction centers under variable light conditions. This model combines the processes of photoproduction and photoinhibition in the Han model with a novel mathematical representation of photoprotective mechanisms, including qE-quenching and qI-quenching. For calibration and validation purposes, the model can be used to simulate fluorescence fluxes, such as those measured in PAM fluorometry, as well as classical fluorescence indexes. A calibration is carried out for the microalga Nannochloropsis gaditana, whereby 9 out of the 13 model parameters are estimated with good statistical significance using the realized, minimal and maximal fluorescence fluxes measured from a typical PAM protocol. The model is further validated by considering a more challenging PAM protocol alternating periods of intense light and dark, showing a good ability to provide quantitative predictions of the fluorescence fluxes even though it was calibrated for a different and somewhat simpler PAM protocol. A promising application of the model is for the prediction of PI-response curves based on PAM fluorometry, together with the long-term prospect of combining it with hydrodynamic and light attenuation models for high-fidelity simulation and optimization of full-scale microalgae production systems

Hybrid modeling of a biorefinery separation process to monitor short-term and long-term membrane fouling

Membrane filtration is commonly used in biorefineries to separate cells from fermentation broths containing the desired products. However, membrane fouling can cause short-term process disruption and long-term membrane degradation. The evolution of membrane resistance over time can be monitored to track fouling, but this calls for adequate sensors in the plant. This requirement might not be fulfilled even in modern biorefineries, especially when multiple, tightly interconnected membrane modules are used. Therefore, characterization of fouling in industrial facilities remains a challenge. In this study, we propose a hybrid modeling strategy to characterize both reversible and irreversible fouling in multi-module biorefinery membrane separation systems. We couple a linear data-driven model, to provide high-frequency estimates of trans-membrane pressures from the available measurements, with a simple nonlinear knowledge-driven model, to compute the resistances of the individual membrane modules. We test the proposed strategy using real data from the world's first industrial biorefinery manufacturing 1,4-bio-butanediol via fermentation of renewable raw materials. We show how monitoring of individual resistances, even when done by simple visual inspection, offers valuable insight on the reversible and irreversible fouling state of the membranes. We also discuss the advantage of the proposed approach, over monitoring trans-membrane pressures and permeate fluxes, from the standpoints of data variability, effect of process changes, interaction between module in multi-module systems, and fouling dynamics

A model‐based protocol for the diagnosis of von Willebrand disease

Von Willebrand disease (VWD) is one of the main inherited coagulation disorders. It is caused by a deficiency and/or a dysfunction of the von Willebrand factor (VWF), a fundamental multimeric glycoprotein involved in the hemostasis process. Correct detection of the disease is not an easy task because the disease manifests itself in many variants and a high intra-subject variability is observed. For these reasons, the diagnostic clinical trials typically rely on a 24-h sampling protocol, which makes the overall test long, stressful, and costly. Using a new pharmacokinetic model derived from Galvanin et al.'s 2014 study, this study aims at i) assessing the theoretical possibility to perform a shorter clinical test and ii) proposing a set of model-based diagnostic methods as a support for the clinical team. A preliminary information analysis is performed in order to understand which sampling instants are more informative for model identification. This allowed us to propose a novel, 8-h diagnostic protocol, which is still able to ensure model identifiability. Three alternative diagnostic methods are then proposed based on this short-length clinical protocol. One of them directly uses the pharmacokinetic model, whereas the other two are based on the use of three indices (two pharmacokinetic indices, namely clearance, total VWF released, and as third index the basal multimer ratio) to formulate the diagnosis problem as a classification one. The classification problem is then solved using K-nearest neighbours and linear discriminant analysis. Results show the theoretical feasibility of a VWD diagnosis based on a shorter protocol

Robotic Wireless Sensor Networks

In this chapter, we present a literature survey of an emerging, cutting-edge, and multi-disciplinary field of research at the intersection of Robotics and Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system that aims to achieve certain sensing goals while meeting and maintaining certain communication performance requirements, through cooperative control, learning and adaptation. While both of the component areas, i.e., Robotics and WSN, are very well-known and well-explored, there exist a whole set of new opportunities and research directions at the intersection of these two fields which are relatively or even completely unexplored. One such example would be the use of a set of robotic routers to set up a temporary communication path between a sender and a receiver that uses the controlled mobility to the advantage of packet routing. We find that there exist only a limited number of articles to be directly categorized as RWSN related works whereas there exist a range of articles in the robotics and the WSN literature that are also relevant to this new field of research. To connect the dots, we first identify the core problems and research trends related to RWSN such as connectivity, localization, routing, and robust flow of information. Next, we classify the existing research on RWSN as well as the relevant state-of-the-arts from robotics and WSN community according to the problems and trends identified in the first step. Lastly, we analyze what is missing in the existing literature, and identify topics that require more research attention in the future

Optimal European cooperative supply chains for carbon capture, transport, and sequestration with costs share policies

In the past decades, CO2 constituted nearly the 80% of anthropogenic greenhouse gases emissions therefore, global actions are needed to tackle the increase of carbon concentration in the atmosphere. CO2 (carbon) capture and storage has been highlighted among the most promising options to decarbonize the energy and industry sectors. Considering a large-scale infrastructure at European level, economic cooperation has been highlighted as a key requirement to relieve single countries from too high risk and commitment. This article proposes an economic optimization for cooperative supply chains for CO2 capture and storage, by adopting policies that balance the spread of costs among countries, according to local characteristics in terms of population, CO2 emissions, and macroeconomic outcome. Results show that the additional European investment for cooperation (max. +2.6% with respect to a noncooperative network) should not constitute a barrier toward the installation and operation of such more effective network designs

A framework for PLS-SIM integration

A novel algorithm is presented for the design of inferential estimators forprocess monitoring and control. The algorithm aims at integrating Partial Least Squares (PLS) techniques and Subspace Identification Methods (SIM) to exploit the main advantages of both methodologies. In particular, the algorithm will retain the PLS computational robustness in dealing with large sets of correlated inputs and outputs, whilst profiting by the SIM dynamic description of the system being investigated