000			01963nam a2200217Ia 4500
005			20250429104902.0
008			230228s1999||||xx |||||||||||||| ||eng||
020			_a9780884152552
041			_aEnglish
082			_a660.2815 S43
100			_aNarasimhan, Shankar _eAuthor _96373
100			_aJordache, Cornelius _eCo-Author _96374
245		0	_aData reconciliation & gross error detection an intelligent use of process Data
260			_aHouston: _bGulf Professional Publishing, _c1999.
300			_a406p.; 23cms.
500			_aThis book provides a systematic and comprehensive treatment of the variety of methods available for applying data reconciliation techniques. Data filtering, data compression and the impact of measurement selection on data reconciliation are also exhaustively explained. Data errors can cause big problems in any process plant or refinery. Process measurements can be corrupted by power supply fluctuations, network transmission and signal conversion noise, analog input filtering, changes in ambient conditions, instrument malfunctioning, miscalibration, and the wear and corrosion of sensors, among other factors. Here's a book that helps you detect, analyze, solve, and avoid the data acquisition problems that can rob plants of peak performance. This indispensable volume provides crucial insights into data reconciliation and gross error detection techniques that are essential for optimal process control and information systems. This book is an invaluable tool for engineers and managers faced with the selection and implementation of data reconciliation software, or for those developing such software. For industrial personnel and students, Data Reconciliation and Gross Error Detection is the ultimate reference.
650			_a Measurement Errors and Error Reduction Techniques _96375
650			_aIntroduction to Gross Error Detection _96376
650			_a Linear Steady-State Data Reconciliation _96377
942			_cBK
999			_c415 _d415