Abstract-In this paper a physical-layer network coded twoway relay system applying Low-Density Parity-Check (LDPC) codes for error correction is considered, where two sources A and B desire to exchange information with each other by the help of a relay R. The critical process in such a system is the calculation of the network-coded transmit word at the relay on basis of the superimposed channel-coded words of the two sources. For this joint channel-decoding and network-encoding task a generalized Sum-Product Algorithm (SPA) is developed. This novel iterative decoding approach outperforms other recently proposed schemes as demonstrated by simulation results.
Dynamic optimization techniques are applied for the optimization of crystallization processes.
These obtain promising results, especially for difficult industrial applications with significant
heat effects, concentrated slurries, and state constraints. Here we introduce some concepts that
focus not only on the optimization strategy but also on the practical implementation. As a case
study, we consider a batch crystallization process, which has been studied in the field. The
dynamic model includes not only moment equations but also thermodynamic equations to make
the model closer to practical operating characteristics. Significant differences between this
research and previous work are that we incorporate the heat-transfer components and control
directly into the model. After demonstrating in plant that the dynamic model is valid in both
model formulation and parameter identification, we optimize this model. The objective is to
maximize the final crystal size in order to obtain the highest purity of the desired product. Here,
we use the package DynoPC, which includes recently developed dynamic optimization strategies.
The dynamic model, consisting of differential and algebraic equations, is discretized using
collocation on finite elements. The resulting nonlinear programming problem is solved with a
reduced successive quadratic programming algorithm. The results are then compared with those
obtained using a maximum principle for minimum operation time and with previous plant
operation profiles. The optimal results show important improvements as the mean size of the
crystals is 50% larger than the ones obtained under original operating conditions.
It is well-known that distributed parameter computational fluid dynamics (CFD) models provide more accurate results than conventional, lumped-parameter unit operation models used in process simulation. Consequently, the use of CFD models in process/equipment co-simulation offers the potential to optimize overall plant performance with respect to complex thermal and fluid flow phenomena. Because solving CFD models is time-consuming compared to the overall process simulation, we consider the development of fast reduced order models (ROMs) based on CFD results to closely approximate the high-fidelity equipment models in the co-simulation. By considering process equipment items with complicated geometries and detailed thermodynamic property models, this study proposes a strategy to develop ROMs based on principal component analysis (PCA). Taking advantage of commercial process simulation and CFD software (for example, Aspen Plus and FLUENT), we are able to develop systematic CFD-based ROMs for equipment models in an efficient manner. In particular, we show that the validity of the ROM is more robust within well-sampled input domain and the CPU time is significantly reduced. Typically, it takes at most several CPU seconds to evaluate the ROM compared to several CPU hours or more to solve the CFD model. Two case studies, involving two power plant equipment examples, are described and demonstrate the benefits of using our proposed ROM methodology for process simulation and optimization.
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