High detail stationary optimization models for gas networks: validation and results

Due to strict regulatory rules in combination with complex nonlinear physics, major gas network operators in Germany and Europe face hard planning problems that call for optimization. In part 1 of this paper we have developed a suitable model hierarchy for that purpose. Here we consider the more practical aspects of modeling. We validate individual model components against a trusted simulation tool, give a structural overview of the model hierarchy, and use its large variety of approximations to devise robust and efficient solution techniques. An extensive computational study demonstrates the suitability of our models and techniques for previously unsolvable problems in gas network planning.     

A constraint-based variability modeling framework

Constraint-based variability modeling is a flexible, declarative approach to managing solution-space variability. Product variants are defined in a top-down manner by successively restricting the admissible combinations of product artifacts until a specific product variant is determined. In this paper, we illustrate the range of constraint-based variability modeling by discussing two of its extreme flavors: constraint-guarded variability modeling and constraint-driven variability modeling. The former applies model checking to establish the global consistency of product variants which are built by manual specification of variations points, whereas the latter uses synthesis technology to fully automatically generate product variants that satisfy all given constraints. Each flavor is illustrated by means of a concrete case study.     

The impact of the number of parallel warehouses on total inventory

In the strategic design of a distribution system, the right number of stock points for the various products is an important question. In the past decade, a strong trend in the consumer goods industry led to centralizing the inventory in a single echelon consisting of a few parallel warehouses or even a single distribution center for a Europe-wide distribution system. Centralizing inventory is justified by the reduction in total stock which mostly overcompensates the increasing transportation cost. The effect of centralization is usually described by the “Square Root Law”, stating that the total stock increases with the square root of the number of stock points. However, in the usual case where the warehouses are replenished in full truck loads and where a given fill rate has to be satisfied, the Square Root Law is not valid. This paper explores that case. It establishes functional relationships between the demand to be served by a warehouse and the necessary safety and cycle stock for various demand settings and control policies, using an approximation of the normal loss function and its inverse. As a consequence, the impact of the number of parallel warehouses on the total stock can be derived. The results can be used as tools in network design models.     

CFD Process Modeling of the Industrial Calcination of Diatomite

Calcination of diatomite is an expensive process frequently resulting in products with unpredictable structure. Alternatively, calcination in swirling flow is an energy‐saving option. Computational fluid dynamics modeling of an experimental calcination process unit is presented. Experimental results and systematic collection of process data were used to define boundary condition for steady‐state and transient simulation runs. The comparison of experimental and simulation results shows the complexity of the calcination process. The results can be used for further process optimization.     

Differential Impact of Hyperglycemia in Critically Ill Patients: Significance in Acute Myocardial Infarction but Not in Sepsis?

Hyperglycemia is a common condition in critically ill patients admitted to an intensive care unit (ICU). These patients represent an inhomogeneous collective and hyperglycemia might need different evaluation depending on the underlying disorder. To elucidate this, we investigated and compared associations of severe hyperglycemia (>200 mg/dL) and mortality in patients admitted to an ICU for acute myocardial infarction (AMI) or sepsis as the two most frequent admission diagnoses. From 2006 to 2009, 2551 patients 69 (58–77) years; 1544 male; 337 patients suffering from type 2 diabetes (T2DM)) who were admitted because of either AMI or sepsis to an ICU in a tertiary care hospital were investigated retrospectively. Follow-up of patients was performed between May 2013 and November 2013. In a Cox regression analysis, maximum glucose concentration at the day of admission was associated with mortality in the overall cohort (HR = 1.006, 95% CI: 1.004–1.009; p < 0.001) and in patients suffering from myocardial infarction (HR = 1.101, 95% CI: 1.075–1.127; p < 0.001) but only in trend in patients admitted to an ICU for sepsis (HR = 1.030, 95% CI: 0.998–1.062; p = 0.07). Severe hyperglycemia was associated with adverse intra-ICU mortality in the overall cohort (23% vs. 13%; p < 0.001) and patients admitted for AMI (15% vs. 5%; p < 0.001) but not for septic patients (39% vs. 40%; p = 0.48). A medical history of type 2 diabetes (n = 337; 13%) was not associated with increased intra-ICU mortality (15% vs. 15%; p = 0.93) but in patients with severe hyperglycemia and/or a known medical history of type 2 diabetes considered in combination, an increased mortality in AMI patients (intra-ICU 5% vs. 13%; p < 0.001) but not in septic patients (intra-ICU 38% vs. 41%; p = 0.53) could be evidenced. The presence of hyperglycemia in critically ill patients has differential impact within the different etiological groups. Hyperglycemia in AMI patients might identify a sicker patient collective suffering from pre-diabetes or undiagnosed diabetes with its’ known adverse consequences, especially in the long-term. Hyperglycemia in sepsis might be considered as adaptive survival mechanism to hypo-perfusion and consecutive lack of glucose in peripheral cells. AMI patients with hyperglycemic derailment during an ICU-stay should be closely followed-up and extensively screened for diabetes to improve patients’ outcome.