Revitalizing Comprehensivization: The Berlin School Reform of 2009

Honors (Bachelor's)GermanUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/120619/1/mhshort.pd

Synthesis of optimal heat and mass exchange networks using a two-step hybrid approach including detailed unit designs

This PhD thesis develops a methodology for the synthesis of optimal heat and mass exchanger networks through a novel hybrid method. The two-step procedure makes use of simplified exchanger models in a network optimisation step, followed by a detailed design where the exchangers found in the network synthesis step are modelled in detail. Subsequent iterations of the network design step are then updated with information from the detailed network designs. The algorithm has certain advantages over previous methods in that the network optimisation is based on more realistic representations of the actual units therein and also that the method increases the likelihood of attaining a globally optimal solution through the generation and assessment of multiple candidate networks throughout the algorithm. The method can be used in a variety of applications and is demonstrated to be effective for large problems and multi-period scenarios. The thesis also shows that the method can be used in conjunction with multiple individual unit optimisation techniques including heuristics and fully explicit optimisation methods

A simplified approach to Multivariable Model Predictive Control

The benefits of applying the range of technologies generally known as Model Predictive Control (MPC) to the control of industrial processes have been well documented in recent years. One of the principal drawbacks to MPC schemes are the relatively high on-line computational burdens when used with adaptive, constrained and/or multivariable processes, which has warranted some researchers and practitioners to seek simplified approaches for its implementation. To date, several schemes have been proposed based around a simplified 1-norm formulation of multivariable MPC, which is solved online using the simplex algorithm in both the unconstrained and constrained cases. In this paper a 2-norm approach to simplified multivariable MPC is formulated, which is solved online using a vector-matrix product or a simple iterative coordinate descent algorithm for the unconstrained and constrained cases respectively. A CARIMA model is employed to ensure offset-free control, and a simple scheme to produce the optimal predictions is described. A small simulation study and further discussions help to illustrate that this quadratic formulation performs well and can be considered a useful adjunct to its linear counterpart, and still retains the beneficial features such as ease of computer-based implementation

Move Suppression Calculations for Well-Conditioned MPC

Several popular tuning strategies applicable to Model Predictive Control (MPC) schemes such as GPC and DMC have previously been developed. Many of these tuning strategies require an approximate model of the controlled process to be obtained, typically of the First Order Plus Dead Time type. One popular method uses such a model to analytically calculate an approximate value of the move suppression coefficient to achieve a desired condition number for the regularized system dynamic matrix; however it is not always accurate and tends to under-estimate the required value. In this paper an off-line method is presented to exactly calculate the move suppression coefficient required to achieve a desired condition number directly from the unregularized system dynamic matrix. This method involves an Eigendecomposition of the system dynamic matrix - which may be too unwieldy in some cases –and a simpler analytical expression is also derived. This analytical expression provides a guaranteed tight upper bound on the required move suppression coefficient yielding a tuning formula which is easy to apply, even in on-line situations. Both methods do not require the use of approximate or reduced order process models for their application. Simulation examples and perturbation studies illustrate the effectiveness of the methods in both off-line and on-line MPC configurations. It is shown that accurate conditioning and improved closed loop robustness can be achieved

Bounds on Worst-Case Deadline Failure Probabilities in Controller Area Networks

Industrial communication networks like the Controller Area Network (CAN) are often required to operate reliably in harsh environments which expose the communication network to random errors. Probabilistic schedulability analysis can employ rich stochastic error models to capture random error behaviors, but this is most often at the expense of increased analysis complexity. In this paper, an efficient method (of time complexity O(n log n)) to bound the message deadline failure probabilities for an industrial CAN network consisting of n periodic/sporadic message transmissions is proposed. The paper develops bounds for Deadline Minus Jitter Monotonic (DMJM) and Earliest Deadline First (EDF) message scheduling techniques. Both random errors and random bursts of errors can be included in the model. Stochastic simulations and a case study considering DMJM and EDF scheduling of an automotive benchmark message set provide validation of the technique and highlight its application

Eligible earliest deadline first:Server-based scheduling for master-slave industrial wireless networks

Industrial automation and control systems are increasingly deployed using wireless networks in master-slave, star-type configurations that employ a slotted timeline schedule. In this paper, the scheduling of (re)transmissions to meet real-time constraints in the presence of non-uniform interference in such networks is considered. As packet losses often occur in correlated bursts, it is often useful to insert gaps before attempting retransmissions. In this paper, a quantum Earliest Deadline First (EDF) scheduling framework entitled ‘Eligible EDF’ is suggested for assigning (re)transmissions to available timeline slots by the master node. A simple but effective server strategy is introduced to reclaim unused channel utilization and replenish failed slave transmissions, a strategy which prevents cascading failures and naturally introduces retransmission gaps. Analysis and examples illustrate the effectiveness of the proposed method. Specifically, the proposed framework gives a timely throughput of 99.81% of the timely throughput that is optimally achievable using a clairvoyant scheduler

Preliminary Checklist of the Fishes of the Illinois River, Arkansas

A survey of the fishes of the mainstream of the Illinois River in northwestern Arkansas produced 51 species representing 11 families. Four of these species, Ictiobus bubalus, smallmouth buffalo; Moxostoma carinatum, river redhorse; Lepomis gulosus, warmouth; and Percina phoxocephala, slenderhead darter, have not been recorded previously from the Arkansas part of the Illinois. Eleven additional species have been reported previously that were not collected during this survey, for a total of 62 species known in the Illinois River