Dynamic models for start-up operations of batch distillation columns with experimental validation

The simulation of batch distillation columns during start-up operations is a very challenging modelling problem because of the complex dynamic behaviour. Only few rigorous models for distillation columns start-up are available in literature and generally required a lot of parameters related to tray or pack geometry. On an industrial viewpoint, such a complexity penalizes the achievement of a fast and reliable estimate of start-up periods. In this paper, two “simple” mathematical models are proposed for the simulation of the dynamic behaviour during start-up operations from an empty cold state. These mathematical models are based on a rigorous tray-by-tray description of the column described by conservation laws, liquid–vapour equilibrium relationships and equations representative of hydrodynamics. The models calibration and validation are studied through experiments carried out on a batch distillation pilot plant, with perforated trays, supplied by a water methanol mixture. The proposed models are shown by comparison between simulation and experimental studies to provide accurate and reliable representations of the dynamic behaviour of batch distillation column start-ups, in spite of the few parameters entailed

Influence of solvent choice on the optimisation of a reaction–separation operation : application to a Beckmann rearrangement reaction

In pharmaceutical syntheses, the solvent choice generally represents a complex design step. Traditionally, this choice is operated according to criteria connected with the reaction step and without any consideration on the following separation steps. The purpose of this study is to highlight the benefits of a global approach of optimisation for the solvent determination. In this way, an optimisation framework dedicated to global synthesis is applied to a simple reaction–separation operation integrating a Beckmann rearrangement reaction, leading to interesting solvent choices

Selection of sensors by a new methodology coupling a classification technique and entropy criteria

Complex industrial processes invest a lot of money in sensors and automation devices to monitor and supervise the process in order to guarantee the production quality and the plant and operators safety. Fault detection is one of the multiple tasks of process monitoring and it critically depends on the sensors that measure the significant process variables. Nevertheless, most of the works on fault detection and diagnosis found in literature emphasis more on developing procedures to perform diagnosis given a set of sensors, and less on determining the actual location of sensors for efficient identification of faults. A methodology based on learning and classification techniques and on the information quantity measured by the Entropy concept, is proposed in order to address the problem of sensor location for fault identification. The proposed methodology has been applied to a continuous intensified reactor, the "Open Plate Reactor (OPR)", developed by Alfa Laval and studied at the Laboratory of Chemical Engineering of Toulouse. The different steps of the methodology are explained through its application to the carrying out of an exothermic reaction

Modeling and Optimization of Lactic Acid Synthesis by the Alkaline Degradation of Fructose in a Batch Reactor

The present work deals with the determination of the optimal operating conditions of lactic acid synthesis by the alkaline degradation of fructose. It is a complex transformation for which detailed knowledge is not available. It is carried out in a batch or semi-batch reactor. The ‘‘Tendency Modeling’’ approach, which consists of the development of an approximate stoichiometric and kinetic model, has been used. An experimental planning method has been utilized as the database for model development. The application of the experimental planning methodology allows comparison between the experimental and model response. The model is then used in an optimization procedure to compute the optimal process. The optimal control problem is converted into a nonlinear programming problem solved using the sequencial quadratic programming procedure coupled with the golden search method. The strategy developed allows simultaneously optimizing the different variables, which may be constrained. The validity of the methodology is illustrated by the determination of the optimal operating conditions of lactic acid production

Bivariate genetic modelling of the response to an oral glucose tolerance challenge: A gene x environment interaction approach

AIMS/HYPOTHESIS: Twin and family studies have shown the importance of genetic factors influencing fasting and 2 h glucose and insulin levels. However, the genetics of the physiological response to a glucose load has not been thoroughly investigated. METHODS: We studied 580 monozygotic and 1,937 dizygotic British female twins from the Twins UK Registry. The effects of genetic and environmental factors on fasting and 2 h glucose and insulin levels were estimated using univariate genetic modelling. Bivariate model fitting was used to investigate the glucose and insulin responses to a glucose load, i.e. an OGTT. RESULTS: The genetic effect on fasting and 2 h glucose and insulin levels ranged between 40% and 56% after adjustment for age and BMI. Exposure to a glucose load resulted in the emergence of novel genetic effects on 2 h glucose independent of the fasting level, accounting for about 55% of its heritability. For 2 h insulin, the effect of the same genes that already influenced fasting insulin was amplified by about 30%. CONCLUSIONS/INTERPRETATION: Exposure to a glucose challenge uncovers new genetic variance for glucose and amplifies the effects of genes that already influence the fasting insulin level. Finding the genes acting on 2 h glucose independently of fasting glucose may offer new aetiological insight into the risk of cardiovascular events and death from all causes

Models and algorithms for energy-efficient scheduling with immediate start of jobs

We study a scheduling model with speed scaling for machines and the immediate start requirement for jobs. Speed scaling improves the system performance, but incurs the energy cost. The immediate start condition implies that each job should be started exactly at its release time. Such a condition is typical for modern Cloud computing systems with abundant resources. We consider two cost functions, one that represents the quality of service and the other that corresponds to the cost of running. We demonstrate that the basic scheduling model to minimize the aggregated cost function with n jobs is solvable in O(nlogn) time in the single-machine case and in O(n²m) time in the case of m parallel machines. We also address additional features, e.g., the cost of job rejection or the cost of initiating a machine. In the case of a single machine, we present algorithms for minimizing one of the cost functions subject to an upper bound on the value of the other, as well as for finding a Pareto-optimal solution

Assertion-based proof checking of Chang-Roberts leader election in PVS

We report a case study in automated incremental assertion-based proof checking with PVS. Given an annotated distributed algorithm, our tool ProPar generates the proof obligations for partial correctness, plus a proof script per obligation. ProPar then lets PVS attempt to discharge all obligations by running the proof scripts. The Chang-Roberts algorithm elects a leader on a unidirectional ring with unique identities. With ProPar, we check its correctness with a very high degree of automation: over 90% of the proof obligations is discharged automatically. This case study underlines the feasibility of the approach and is, to the best of our knowledge, the first verification of the Chang-Roberts algorithm for arbitrary ring size in a proof checker

An exercise-based international polymer syllabus

The IUPAC Subcommittee on Polymer Education has been pursuing the development of a compact syllabus covering the essential topics required for a tertiary education in polymer science, with numerical and short answer exercises addressing each topic. The primary goal of the document is to provide a framework for a complete course made freely available worldwide so that any educator can implement a professionally-curated course in polymer science for their students without needing expensive textbooks or reliable internet access. An important secondary goal is to popularize the use of approved IUPAC terminology in polymer science by using it consistently throughout the document and providing references to IUPAC source documents. Professor Melissa Chin Han Chan was an active and enthusiastic participant in the project who played a significant role in its design and implementation. The late Professor Richard ‘Dick’ Jones also had a keen interest in the project and had a great influence on its direction and structure. This brief note is dedicated to these two illustrious polymer scientists

Integrative Analysis Reveals a Molecular Stratification of Systemic Autoimmune Diseases

Objective Clinical heterogeneity, a hallmark of systemic autoimmune diseases, impedes early diagnosis and effective treatment, issues that may be addressed if patients could be classified into groups defined by molecular pattern. This study was undertaken to identify molecular clusters for reclassifying systemic autoimmune diseases independently of clinical diagnosis. Methods Unsupervised clustering of integrated whole blood transcriptome and methylome cross-sectional data on 955 patients with 7 systemic autoimmune diseases and 267 healthy controls was undertaken. In addition, an inception cohort was prospectively followed up for 6 or 14 months to validate the results and analyze whether or not cluster assignment changed over time. Results Four clusters were identified and validated. Three were pathologic, representing “inflammatory,” “lymphoid,” and “interferon” patterns. Each included all diagnoses and was defined by genetic, clinical, serologic, and cellular features. A fourth cluster with no specific molecular pattern was associated with low disease activity and included healthy controls. A longitudinal and independent inception cohort showed a relapse–remission pattern, where patients remained in their pathologic cluster, moving only to the healthy one, thus showing that the molecular clusters remained stable over time and that single pathogenic molecular signatures characterized each individual patient. Conclusion Patients with systemic autoimmune diseases can be jointly stratified into 3 stable disease clusters with specific molecular patterns differentiating different molecular disease mechanisms. These results have important implications for future clinical trials and the study of nonresponse to therapy, marking a paradigm shift in our view of systemic autoimmune diseases

IGF-I induced genes in stromal fibroblasts predict the clinical outcome of breast and lung cancer patients

<p>Abstract</p> <p>Background</p> <p>Insulin-like growth factor-1 (IGF-I) signalling is important for cancer initiation and progression. Given the emerging evidence for the role of the stroma in these processes, we aimed to characterize the effects of IGF-I on cancer cells and stromal cells separately.</p> <p>Methods</p> <p>We used an <it>ex vivo </it>culture model and measured gene expression changes after IGF-I stimulation with cDNA microarrays. <it>In vitro </it>data were correlated with <it>in vivo </it>findings by comparing the results with published expression datasets on human cancer biopsies.</p> <p>Results</p> <p>Upon stimulation with IGF-I, breast cancer cells and stromal fibroblasts show some common and other distinct response patterns. Among the up-regulated genes in the stromal fibroblasts we observed a significant enrichment in proliferation associated genes. The expression of the IGF-I induced genes was coherent and it provided a basis for the segregation of the patients into two groups. Patients with tumours with highly expressed IGF-I induced genes had a significantly lower survival rate than patients whose tumours showed lower levels of IGF-I induced gene expression (<it>P </it>= 0.029 - Norway/Stanford and <it>P </it>= 7.96e-09 - NKI dataset). Furthermore, based on an IGF-I induced gene expression signature derived from primary lung fibroblasts, a separation of prognostically different lung cancers was possible (<it>P </it>= 0.007 - Bhattacharjee and <it>P </it>= 0.008 - Garber dataset).</p> <p>Conclusion</p> <p>Expression patterns of genes induced by IGF-I in primary breast and lung fibroblasts accurately predict outcomes in breast and lung cancer patients. Furthermore, these IGF-I induced gene signatures derived from stromal fibroblasts might be promising predictors for the response to IGF-I targeted therapies.</p> <p>See the related commentary by Werner and Bruchim: <url>http://www.biomedcentral.com/1741-7015/8/2</url></p