MINLP Model for the Synthesis of Heat Exchanger Networks with Handling Pressure of Process Streams

This paper introduces a new mathematical model for the simultaneous synthesis of heat exchanger networks (HENs), wherein the handling pressure of process streams is used to enhance the heat integration. The proposed approach combines generalized disjunctive programming (GDP) and mixed-integer nonlinear programming (MINLP) formulation, in order to minimize the total annualized cost composed by operational and capital expenses. A multi-stage superstructure is developed for the HEN synthesis, assuming constant heat capacity flow rates and isothermal mixing, and allowing for streams splits. In this model, the pressure and temperature of streams must be treated as optimization variables, increasing further the complexity and difficulty to solve the problem. In addition, the model allows for coupling of compressors and turbines to save energy. A case study is performed to verify the accuracy of the proposed model. In this example, the optimal integration between the heat and work decreases the need for thermal utilities in the HEN design. As a result, the total annualized cost is also reduced due to the decrease in the operational expenses related to the heating and cooling of the streams.

3D model reconstruction using neural gas accelerated on GPU

In this work, we propose the use of the neural gas (NG), a neural network that uses an unsupervised Competitive Hebbian Learning (CHL) rule, to develop a reverse engineering process. This is a simple and accurate method to reconstruct objects from point clouds obtained from multiple overlapping views using low-cost sensors. In contrast to other methods that may need several stages that include downsampling, noise filtering and many other tasks, the NG automatically obtains the 3D model of the scanned objects. To demonstrate the validity of our proposal we tested our method with several models and performed a study of the neural network parameterization computing the quality of representation and also comparing results with other neural methods like growing neural gas and Kohonen maps or classical methods like Voxel Grid. We also reconstructed models acquired by low cost sensors that can be used in virtual and augmented reality environments for redesign or manipulation purposes. Since the NG algorithm has a strong computational cost we propose its acceleration. We have redesigned and implemented the NG learning algorithm to fit it onto Graphics Processing Units using CUDA. A speed-up of 180× faster is obtained compared to the sequential CPU version.

Validation of the Spanish version of mackey childbirth satisfaction rating scale

Background: The “Mackey Childbirth Satisfaction Rating Scale” (MCSRS) is a complete non-validated scale which includes the most important factors associated with maternal satisfaction. Our primary purpose was to describe the internal structure of the scale and validate the reliability and validity of concept of its Spanish version MCSRS-E. Methods: The MCSRS was translated into Spanish, back-translated and adapted to the Spanish population. It was then administered following a pilot test with women who met the study participant requirements. The scale structure was obtained by performing an exploratory factorial analysis using a sample of 304 women. The structures obtained were tested by conducting a confirmatory factorial analysis using a sample of 159 women. To test the validity of concept, the structure factors were correlated with expectations prior to childbirth experiences. McDonald’s omegas were calculated for each model to establish the reliability of each factor. The study was carried out at four University Hospitals; Alicante, Elche, Torrevieja and Vinalopo Salud of Elche. The inclusion criteria were women aged 18–45 years old who had just delivered a singleton live baby at 38–42 weeks through vaginal delivery. Women who had difficulty speaking and understanding Spanish were excluded. Results: The process generated 5 different possible internal structures in a nested model more consistent with the theory than other internal structures of the MCSRS applied hitherto. All of them had good levels of validation and reliability. Conclusions: This nested model to explain internal structure of MCSRS-E can accommodate different clinical practice scenarios better than the other structures applied to date, and it is a flexible tool which can be used to identify the aspects that should be changed to improve maternal satisfaction and hence maternal health.