Mani Bhushan

Design of sensor location based on various fault diagnostic observability and reliability criteria

Abstract The success of any fault diagnosis technique depends critically on the sensors measuring the important process variables. To select the optimum number of sensors for a given process, qualitative (cause—effect), graph theoretic approaches, based on process digraph (DG) were proposed. Graph approaches that used the concept of observability (detecting all faults) and resolution (identifying the exact fault) were discussed. In this article, besides briefly summarizing these approaches, a reliability formulation for selecting optimal sensors is presented. The formulation takes into account quantitative information such as fault occurrence probabilities, sensor failure probabilities, and sensor costs. The formulation can also handle various constraints. Heuristics to solve the posed problem are also discussed. Sensor location based on the proposed formulation is applied to a chemical process to illustrate the utility of the proposed approach.

Design of robust, reliable sensor networks using constraint programming

Abstract Various criteria have been considered in the literature for selection of optimal sensor networks. Amongst these, maximization of network reliability is an important criterion. While there are several approaches for designing maximum reliability networks, uncertainty in the available sensor reliability data has not been considered in these designs. In this article we present two novel formulations that incorporate robustness to uncertainties in the reliability data. Towards this end the sensor network design problem for maximizing reliability is formulated as explicit-optimization (MINLP) problem using failure rates of sensors which have better scaling properties instead of sensor reliabilities. Constraint programming (CP) has been used for solving the resulting optimization problems. Use of CP also enables easy generation of pareto front characterizing trade-offs between performance, cost and robustness for various uncertainty scenarios. The utility of the proposed approach is demonstrated on a case study taken from the literature.

Substrate Uptake, Phosphorus Repression, and Effect of Seed Culture on Glycopeptide Antibiotic Production: Process Model Development and Experimental Validation

Actinomycetes, the soil borne bacteria which exhibit filamentous growth, are known for their ability to produce a variety of secondary metabolites including antibiotics. Industrial scale production of such antibiotics is typically carried out in a multi‐substrate medium where the product formation may experience catabolite repression by one or more of the substrates. Availability of reliable process models is a key bottleneck in optimization of such processes. Here we present a structured kinetic model to describe the growth, substrate uptake and product formation for the glycopeptide antibiotic producer strain Amycolatopsis balhimycina DSM5908. The model is based on the premise that the organism is an optimal strategist and that the various metabolic pathways are regulated via key rate limiting enzymes. Further, the model accounts for substrate inhibition and catabolite repression. The model is also able to predict key phenomena such as simultaneous uptake of glucose and glycerol but with different specific uptake rates, and inhibition of glycopeptide production by high intracellular phosphate levels. The model is successfully applied to both production and seed medium with varying compositions and hence has good predictive ability over a variety of operating conditions. The model parameters are estimated via a well‐designed experimental plan. Adequacy of the proposed model was established via checking the model sensitivity to its parameters and confidence interval calculations. The model may have applications in optimizing seed transfer, medium composition, and feeding strategy for maximizing production. Biotechnol. Bioeng. 2010;105: 109–120.

Multi-objective optimization of glycopeptide antibiotic production in batch and fed batch processes

Fermentation optimization involves potentially conflicting multiple objectives such as product concentration and production media cost. Simultaneous optimization of these objectives would result in a multiobjective optimization problem, which is characterized by a set of multiple solutions, knows as pareto optimal solutions. These solutions gives flexibility in evaluating the trade-offs and selecting the most suitable operating policy. Here, ε-constraint approach was used to generate the pareto solutions for two objectives: product concentration and product per unit cost of media, for batch and fed batch operations using process model for Amycolatopsis balhimycina, a glycopeptide antibiotic producer. This resulted in a set of several pareto optimal solutions with the two objectives ranging from (0.75 g l(-1), 3.97 g

Linear Machine Solution to Point Location Problem

(-1)) to (0.44 g l(-1), 5.19 g

An Improved Gaussian Sum Unscented Kalman Filter

(-1)) for batch and from (1.5 g l(-1), 5.46 g

Linear Machine: A Novel Approach to Point Location Problem

(-1)) to (1.1 g l(-1), 6.34 g

Nonlinear Update Based Unscented Gaussian Sum Filter

(-1)) for fed batch operations. One pareto solution each for batch and for fed batch mode was experimentally validated.

Aerosols cause intraseasonal short-term suppression of Indian monsoon rainfall

Linear machine has been recently proposed as an elegant solution for solving the point location problem arising in multi-parametric programming (mp-P) based online optimization. Linear machine associates a linear discriminant function with each polytopic region in the parametric space. The solution to the point location problem is then obtained by simply evaluating these discriminant functions and finding their maximum value. In this technical note, we rigorously establish the correctness of the linear machine generation procedure and identify a necessary condition for existence of linear machine. A modified procedure, involving systematic subdivision of the parametric space, is proposed when this condition is not satisfied. Analysis of complexity and storage requirements, along with computational experiments on a large sized example, indicate that linear machine can be an efficient tool for solving the point location problem.

Modified Minimum Variance Approach for State and Unknown Input Estimation

Abstract Gaussian sum Unscented Kalman Filter (GS-UKF) is a recently improved estimator for state and parameter estimation of nonlinear dynamical systems. GS-UKF makes use of Unscented Kalman Filter (UKF) as a local filter at each of its individual Gaussians to obtain the local statistics. The UKF based local filters use a limited number of deterministically chosen samples, known as sigma points, to approximate the higher order moments of non-Gaussian prior. However, UKF has an inherent assumption of Gaussian statistics in approximation of non-Gaussian prior. This can undermines the true performance of UKF as well as GS-UKF. In this work, we propose to use Unscented Gaussian Sum Filter (UGSF), at each Gaussian of Gaussian Sum. UGSF uses the same UKF sigma points but approximates the prior with the sum of Gaussians, which can approximate any arbitrary density. We thus label our proposed approach as GS-UGSF. We show the utility of proposed GS-UGSF approach using a nonlinear case study from literature and compare the performance of GS-UGSF with GS-UKF.