The use of computationally demanding knowledge-driven models to optimize a process might encounter substantial numerical challenges. Because a model is an abstraction and approximation of the process, calculating the exact model optimum might not be necessary because its industrial implementation is bound to be an approximate one. Here we are exploring an alternative optimization route through a surrogate model. Because one of the decision variables affecting the optimization is time-varying, the Design of Dynamic Experiments is used to estimate the surrogate model. The process considered here is a freeze-drying process widely used in the pharmaceutical industry. The model used is a stochastic model describing the process in great detail. It is shown that the proposed data-driven route calculates the optimum in about 8 h, as opposed to 22 h for the knowledge-driven model, while sacrificing only < 15% in the computed value of the process performance.

Microscale photobioreactors for microalgae growth represent an interesting technology for fast data production and biomass characterization; however, the small scale poses severe monitoring challenges, as traditional methods cannot be used. Non-invasive techniques are therefore needed to quantify biomass concentration and other culture properties, for example, pigment composition. To this purpose, a soft sensing approach based on multivariate image regression is proposed to exploit RGB images and/or PAM-imaging chlorophyll fluorescence. Different PLS (Partial Least Squares) regression models are used to estimate: (a) biomass concentration from the features extracted by RGB indices and/or PAM-imaging chlorophyll fluorescence measurements; and (b) Chlorophyll a content per cell from the features extracted by RGB indices and biomass concentration measurements. Every single model is aimed at characterizing the microalgae culture at different light intensities during batch growth. Results show that the proposed monitoring approach is as accurate as traditional measurement approaches and may represent a promising methodology for fast and inexpensive monitoring of microscale photobioreactors.

Microfluidic platforms for microalgae cultivation can be exploited to intensify data collection and enhance the ability to control the most relevant operational parameters. In this work, a new array of micro-photobioreactors (mPBR) was designed to supply a continuous flow of nutrients; CO2 is the main substrate for photosynthesis, but its supply in nonlimiting concentration to microdevices can represent a design challenge. To address this issue, a preliminary CO2 absorption phase was exploited. This carbon-rich liquid stream was then delivered to the mPBR, where it diffused to the culture wells. The device was tested with Scenedesmus obliquus using in vivo chlorophyll fluorescence measurements for growth monitoring. Results demonstrated that the experimental setup is effective at delivering CO2 inside the mPBR. Tests at different incident light intensities showed the mPBR applicability to describe light effects on cell growth, without carbon and nutrient limitations and undesired self-shading effects.

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.

Von Willebrand disease (VWD) is the most common inherited bleeding disease and is caused by deficiency or dysfunction of a multimeric glycoprotein, namely the von Willebrand factor (VWF). The disease is present in numerous subtypes, making the diagnosis through the classic 24-h DDAVP sampling protocol a difficult task. In this simulation study, a new simplified pharmacokinetic model is proposed with the aims of demonstrating that a shorter DDAVP clinical test can be devised and that indices, such as clearance, total amount of VWF released and multimeric ratio at basal state can he exploited with multivariate classification methods in order to help practitioners to reach a correct diagnosis.