A new approach to gross error detection provides unbiased estimates and 100( 1 -a ) % simultaneous confidence intervals of process variables when biased process measurements and process leaks exist. Presented in this article are estimation equations for process variables, as well as equations that help identify biased measurements and process leaks. These equations include the power function for a global test, and two types of a-level component tests and their power functions. Important strengths and weaknesses of this approach are compared to those of the serial compensation strategy, in particular, by varying the significance level ( a ) , the variance-covariance matrix ( C ) , the size of measurement bias (6), the number of biased variables, and the sample size ( N ) . Accuracy of 6 estimation and performance in detecting the presence of process leaks ( y ) are also evaluated and compared. The proposed approach has unique features that can provide a basis for improving the reconciliation of variables in process operations.

Hypoglycemia is a major challenge of artificial pancreas systems and a source of concern for potential users and parents of young children with Type 1 diabetes (T1D). Early alarms to warn the potential of hypoglycemia are essential and should provide enough time to take action to avoid hypoglycemia. Many alarm systems proposed in the literature are based on interpretation of recent trends in glucose values. In the present study, subject-specific recursive linear time series models are introduced as a better alternative to capture glucose variations and predict future blood glucose concentrations. These models are then used in hypoglycemia early alarm systems that notify patients to take action to prevent hypoglycemia before it happens. The models developed and the hypoglycemia alarm system are tested retrospectively using T1D subject data. A Savitzky-Golay filter and a Kalman filter are used to reduce noise in patient data. The hypoglycemia alarm algorithm is developed by using predictions of future glucose concentrations from recursive models. The modeling algorithm enables the dynamic adaptation of models to inter-/intra-subject variation and glycemic disturbances and provides satisfactory glucose concentration prediction with relatively small error. The alarm systems demonstrate good performance in prediction of hypoglycemia and ultimately in prevention of its occurrence.

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Many patients with diabetes experience high variability in glucose concentrations that includes prolonged hyperglycemia or hypoglycemia. Models predicting a subject’s future glucose concentrations can be used for preventing such conditions by providing early alarms. This paper presents a time-series model that captures dynamical changes in the glucose metabolism. Adaptive system identification is proposed to estimate model parameters which enable the adaptation of the model to inter-/intra-subject variation and glycemic disturbances. It consists of online parameter identification using the weighted recursive least squares method and a change detection strategy that monitors variation in model parameters. Univariate models developed from a subject’s continuous glucose measurements are compared to multivariate models that are enhanced with continuous metabolic, physical activity and lifestyle information from a multi-sensor body monitor. A real life application for the proposed algorithm is demonstrated on early (30 min in advance) hypoglycemia detection.

This paper extends the method introduced by Rollins et al. (ISA Trans. 1998, 36, 293) to multiple-input, multiple-output systems that give an exact closed-form solution to continuous-time Hammerstein processes written in terms of differential equations and nonlinear inputs. This ability is demonstrated on a theoretical nonlinear Hammerstein process of complex dynamics where perfect identification of the closed-form model is assumed. This paper then demonstrates the simplicity of the proposed identification procedure to obtain an accurate estimate of the exact model using a theoretical Hammerstein model. A powerful attribute of this methodology is the ability to make full use of the statistical design of experiments for optimal data collection and accurate parameter estimation. Application of the proposed method is demonstrated on a household clothes dryer with four input and five output variables. Only 27 trials (input changes) of a central composite design were needed for accurate model development of all five outputs over the input space, and the accurate predictive performance is demonstrated. This paper extends the method introduced by Rollins et al. (ISA Trans. 1998, 36, 293) to multipleinput, multiple-output systems that give an exact closed-form solution to continuous-time Hammerstein processes written in terms of differential equations and nonlinear inputs. This ability is demonstrated on a theoretical nonlinear Hammerstein process of complex dynamics where perfect identification of the closed-form model is assumed. This paper then demonstrates the simplicity of the proposed identification procedure to obtain an accurate estimate of the exact model using a theoretical Hammerstein model. A powerful attribute of this methodology is the ability to make full use of the statistical design of experiments for optimal data collection and accurate parameter estimation. Application of the proposed method is demonstrated on a household clothes dryer with four input and five output variables. Only 27 trials (input changes) of a central composite design were needed for accurate model development of all five outputs over the input space, and the accurate predictive performance is demonstrated.

This paper introduces a methodology for obtaining accurate continuous-time multiple-input, multiple-output models of nonlinear dynamic systems with Wiener characteristics. The models are obtained from complete reliance on experimental data, and this work demonstrates the effectiveness of optimal statistical design of experiments (SDOE) to fully obtain Wiener models. This method is evaluated on a highly nonlinear continuous stirred tank reactor, and its performance is compared to conventional discrete-time Wiener modeling (DTM) using a pseudo-random sequence design (PRSD) and the same SDOE as the proposed method. The proposed method greatly outperforms the DTM developed from PRSD and moderately outperforms the DTM based on SDOE. This paper introduces a methodology for obtaining accurate continuous-time multiple-input, multiple-output models of nonlinear dynamic systems with Wiener characteristics. The models are obtained from complete reliance on experimental data, and this work demonstrates the effectiveness of optimal statistical design of experiments (SDOE) to fully obtain Wiener models. This method is evaluated on a highly nonlinear continuous stirred tank reactor, and its performance is compared to conventional discrete-time Wiener modeling (DTM) using a pseudorandom sequence design (PRSD) and the same SDOE as the proposed method. The proposed method greatly outperforms the DTM developed from PRSD and moderately outperforms the DTM based on SDOE.

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In recent years researchers in many areas have used arti®cial neural networks (ANNs) to model a variety of physical relationships. While in many cases this selection appears sound and reasonable, one must remember than ANN modeling is an empirical modeling technique (based on data) and is subject to the limitations of such techniques. Poor prediction occurs when the training data set does not contain adequate``information'' to model a dynamic process. Using data from a simulated continuousstirred tank reactor, this paper illustrates four scenarios:(1) steady state, (2) large process time constant, (3) infrequent sampling, and (4) variable sampling rate. The ®rst scenario is typical of simulation studies while the other three incorporate attributes found in real plant data. For the cases in which ANNs predicted well, linear regression (LR), one of the oldest empirical modeling techniques, predicted equally well, and when LR failed to accurately model/predict the data, ANNs predicted poorly. Since real plant data would resemble a combination of situations (2), (3), and (4), it is important to understand that empirical models are not necessarily appropriate for predictively modeling dynamic processes in practice. IntroductionArti®cial neural network (ANN) models have recently been used to model a variety of complex nonlinear physical relationships. Empirical techniques that have been employed to predictively model a dynamic process include radial basis function models for a continuous-stirred tank reactor (CSTR) [1], autoregressive moving average with external input (AR-MAX) models for a distillation process [2], an ANN for a continuous-stirred tank fermenter [3], and the recently introduced semi-empirical technique (SET) demonstrated for a CSTR [4]. ANNs fall into the class of purely empirical methods since their structures are seldom, if ever, phenomenologically inspired, and their coef®cients are determined strictly by the data and seldom have physical meaning. As an empirical technique, ANN models are¯exible, relatively easy to ®t, and typically perform very well in applications suited for empirical modeling. However, due to the ease of obtaining accurate ®ts to data sets, they can be misapplied and misused just as easily.This paper demonstrates important limitations of empirical models in accurately predicting model outputs (i.e., responses) to changes in inputs (disturbances) for real data taken from dynamic chemical, or biochemical process. In addition to ANN models, linear regression (LR), the most common empirical modeling technique, is tested. Both ANNs and LR predict well under steady state conditions, with data sampled at every input change, and under dynamic conditions, with constant time intervals between input changes and data sampled at very input change. However, under more complex sampling conditions found in real dynamic plant data such as infrequent sampling and variable sampling rate, empirical methods will likely predict poorly. When process data are sampled infrequently, input changes (there c...