Validation of a multivariable relay-based PID autotuner with specified robustness

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Distributed model predictive control of steam/water loop in large scale ships

In modern steam power plants, the ever-increasing complexity requires great reliability and flexibility of the control system. Hence, in this paper, the feasibility of a distributed model predictive control (DiMPC) strategy with an extended prediction self-adaptive control (EPSAC) framework is studied, in which the multiple controllers allow each sub-loop to have its own requirement flexibility. Meanwhile, the model predictive control can guarantee a good performance for the system with constraints. The performance is compared against a decentralized model predictive control (DeMPC) and a centralized model predictive control (CMPC). In order to improve the computing speed, a multiple objective model predictive control (MOMPC) is proposed. For the stability of the control system, the convergence of the DiMPC is discussed. Simulation tests are performed on the five different sub-loops of steam/water loop. The results indicate that the DiMPC may achieve similar performance as CMPC while outperforming the DeMPC method

Effect of control horizon in model predictive control for steam/water loop in large-scale ships

This paper presents an extensive analysis of the properties of different control horizon sets in an Extended Prediction Self-Adaptive Control (EPSAC) model predictive control framework. Analysis is performed on the linear multivariable model of the steam/water loop in large-scale watercraft/ships. The results indicate that larger control horizon values lead to better loop performance, at the cost of computational complexity. Hence, it is necessary to find a good trade-off between the performance of the system and allocated or available computational complexity. In this original work, this problem is explicitly treated as an optimization task, leading to the optimal control horizon sets for the steam/water loop example. Based on simulation results, it is concluded that specific tuning of control horizons outperforms the case when only a single valued control horizon is used for all the loops

Untargeted LC-QTOF (ESI +) MS Analysis of Small Serum Metabolites Related to Prostate Cancer and Prostate Specific Antigen

Prostate cancer has an increasing incidence and there is an urgent need for development of new serum biomarkers for early diagnostic as the ones known are ineffective. The aim of the study was to use untargeted metabolomics in order to identify and characterize small metabolite fingerprints in patients with normal vs pathologic values of PSA ( previously determined by electrochemiluminiscence). A cohort of one hundred patients with different Prostate Specific amtigen values were investigated by untargeted metabolomics. The serum small metabolite profile determined by high performance liquid chromatography coupled with mass spectrometry, LC-QTOF(ESI+)MS in order to identify specific biomarkers, for normal patient group (PSA = 0-4 ng.ml) and four pathologic groups, having PSA values from 4 to >1000 ng/ml. The major molecules identified in the samples were polar phospholipids, maily lysophosphatidyl choline derivatives, having m/z values from 496 to 524, like LPC(O-16:0/O-1:0), LPC(18:1/2:0) or PS(18:1(9Z)/0:0), LPC(18:2(9Z,12Z)/0:0 and their isomers and  LPC(O-18:1(11Z)/2:0), respectively. Also, small molecules (free fatty acids and prostaglandin derivatives) were identified and are significantly different in pathologic vs normal serum samples. Generally the pathologic samples had increased concentrations of all above mentioned molecules. The Principal Component analysis showed , by plot and loadings scores, significant clustering of normal vs pathological groups