Babji Srinivasan

Stoichiometric control of DNA-grafted colloid self-assembly

Significance Recently, there has been an increased interest in understanding the self-assembly of DNA-grafted colloids into different morphologies. Conventional approaches assume that the critical design parameters are the size and number of DNA grafts on each particle. Stoichiometry is viewed as a secondary variable and its exact role in this context is unresolved. In contrast with these expectations, our experiments show that the equilibrium lattice structure can be tuned through variations in the imposed stoichiometry. These findings are captured through simple extensions of the complementary contact model. Stoichiometry is thus shown to be a powerful handle to control these self-assembled structures. There has been considerable interest in understanding the self-assembly of DNA-grafted nanoparticles into different crystal structures, e.g., CsCl, AlB2, and Cr3Si. Although there are important exceptions, a generally accepted view is that the right stoichiometry of the two building block colloids needs to be mixed to form the desired crystal structure. To incisively probe this issue, we combine experiments and theory on a series of DNA-grafted nanoparticles at varying stoichiometries, including noninteger values. We show that stoichiometry can couple with the geometries of the building blocks to tune the resulting equilibrium crystal morphology. As a concrete example, a stoichiometric ratio of 3:1 typically results in the Cr3Si structure. However, AlB2 can form when appropriate building blocks are used so that the AlB2 standard-state free energy is low enough to overcome the entropic preference for Cr3Si. These situations can also lead to an undesirable phase coexistence between crystal polymorphs. Thus, whereas stoichiometry can be a powerful handle for direct control of lattice formation, care must be taken in its design and selection to avoid polymorph coexistence.

Eye gaze movement studies of control room operators: A novel approach to improve process safety

Abstract Process industries continue to suffer from accidents despite significant regulatory intervention since the mid-1980s. Human error is widely considered to be the major cause for most accidents today. Detailed analysis of various incidents indicates that reduced staffing levels in control rooms and inadequate operator training with complex automation strategies as common reasons for human errors. Therefore, there is a need to develop deeper understanding of human errors as well as strategies to prevent them. However, similar to hardware failures, traditionally human error has been quantified using likelihood approaches; this viewpoint abnegates the role of the cognitive abilities of the operators. Recent studies in other safety critical domains (aviation, health-care) show that operators level of situation awareness as inferred by eye tracking is a good online indicator of human error. In this work, a novel attempt is made to understand the behavior of the operator in a typical chemical plant control room using the information obtained from eye tracker. Experimental studies conducted on 72 participants reveal that fixation patterns contain signatures about the operators learning and awareness at various situations. Implications of these findings on human error in process plant operations them are discussed.

Fast and accurate lithography simulation using cluster analysis in resist model building

Abstract. As technology nodes continue to shrink, optical proximity correction (OPC) has become an integral part of mask design to improve the subwavelength printability. The success of lithography simulation to perform OPC on an entire chip relies heavily on the performance of lithography process models. Any small enhancement in the performance of process models can result in a valuable improvement in the yield. We propose a robust approach for lithography process model building. The proposed scheme uses the clustering algorithm for model building and thereby improves the accuracy and computational efficiency of lithography simulation. The effectiveness of the proposed method is verified by simulating some critical layers in 14- and 22-nm complementary metal oxide semiconductor technology nodes. Experimental results show that compared with a conventional approach, the proposed method reduces the simulation time by 50× with ∼5% improvement in accuracy.

Multivariate Control Loop Performance Assessment With Hurst Exponent and Mahalanobis Distance

A novel data-driven technique for performance assessment of multivariate control loops that takes into account the interactions within the system is proposed. The technique merges the Hurst-exponent-based single-input single-output controller performance index with Mahalanobis distance to devise a multiple-input multiple-output (MIMO) controller performance index. The distinct advantage over the standard minimum variance index and novelty of the proposed approach lies in its ability to quantify the performance of MIMO controller without the knowledge of interactor matrix or system description, which leads to the technique being insensitive to model plant mismatch and easily applicable to nonlinear systems. Only closed-loop routine operating data are required. This new methodology is tested on benchmark systems from the literature and simulation results are presented. Comparison with minimum variance index-based techniques reveals excellent agreement in the trends of both approaches. The results establish the proposed approach as a promising tool for interactor-matrix-independent MIMO control loop performance assessment.

Data driven approach for performance assessment of linear and nonlinear Kalman filters

A new technique is developed for assessing the performance of linear and nonlinear Kalman filter based state estimators. The proposed metric will indicate the performance of these state estimators which will be primarily influenced by: (i) difference between the model dynamics and process dynamics and, (ii) various approximations of the nonlinear plant dynamics used in nonlinear Kalman filters. Currently, there exists no such quantification method to analyze the performance of linear and nonlinear Kalman filters, a key requirement for improvement and a practical benchmark for comparison of these state estimation algorithms. The proposed technique uses the generalized Hurst exponent of the prediction errors (difference in measured output and a posteriori estimates) obtained from the state estimators to quantify the performance. This technique could be implemented on-line as it requires only plant operating data and the predicted outputs (from the linear and nonlinear Kalman filters) to assess the performance. Several simulation studies demonstrate the applicability of the proposed performance metric to both linear and non-linear Kalman filters.

Enhancement of Energy Efficiency at an Indian Milk Processing Plant Using Exergy Analysis

The dairy sector in India is the largest milk producer in the world. Substantial amounts of freshwater and energy are consumed during milk processing with concomitant impacts on sustainability. In this chapter, we study the energy efficiency at India’s largest milk processing plant and propose retrofits for improving the plant’s sustainability. Specifically, we report on exergy analysis of a milk powder manufacturing unit. Exergy of a system at a certain thermodynamic state is the maximum amount of work that can be obtained when the system moves from that state to one of equilibrium with its surroundings. In contrast to a conventional energy analysis, which maps the energy flows of the system and suggests opportunities for process integration, an exergy analysis pinpoints the locations, causes, and magnitudes of thermodynamic losses. The milk powder plant that is the focus of the current study consists of two sections—an evaporation section and a drying section. Our results reveal that exergy efficiency of certain units is very low (<20%). Significant improvements in energy efficiencies can be achieved through simple, low-cost retrofits to these units.

On-line performance monitoring of PEM fuel cell using a fast EIS approach

The Polymer Electrolyte Membrane Fuel Cell is a widely researched fuel cell, and a highly potential candidate for alternate power generation. However, technical issues such as membrane flooding and drying prevent its deployment in many applications. Electrochemical Impedance Spectroscopy (EIS) is a very powerful technique that is used to isolate flooding and drying of the fuel cell from operating data. Such information about the state of operation of the cell is critical to deciding necessary control actions to maintain the health and performance of the cell. However, the time taken in obtaining measurements in EIS can be large enough to allow the cell to flood or dry beyond irreparable damage, rendering it a mere postmortem technique. Moreover, after long durations of perturbation, the cell takes a considerable amount of time to return to its regular operation. A new technique is proposed that uses the concept of EIS, but is computationally faster and gives results comparable with those of traditional EIS. This technique is based on perturbing the cell with a small chirp signal containing large number of frequencies instead of series of small sinusoids at different frequencies. Simulation results on isolation of flooding and drying based on Fast EIS are illustrated and future work directions are indicated.

Towards Obviating Human Errors in Real-time through Eye Tracking

Abstract To minimize human errors (principal reasons for accidents in process industries) it is imperative to understand their cognitive workload, the excess of which is often a preliminary state leading to human errors. In this work, we have devised a methodology based on an eye tracking parameter—gaze entropy—to gauge the variation of cognitive work load on a control room operator. The study highlights the potential of gaze entropy in observing the variation of cognitive workload with learning. The patterns observed have a potential to minimize human errors and improve safety in process industries.

Simulation and Analysis of Indian Residential Electricity Consumption Using Agent-Based Models

Abstract The increasing demand of residential consumption and the integration of renewable energy sources have motivated researchers to develop grid simulations for testing energy management strategies. Agent-based modelling is one such methodology with the capability of mimicking the emergent and complex behaviour of grids over time. Thus, we have utilized this concept to model and predict the energy consumption of a house. Results from the simulation indicate the proposed approach closely mimics the fine-grained energy data obtained from the residential unit in India. This model possesses the flexibility to be extended to estimate the electricity demand of different localities in India and, in step, to understand the behaviour of the agent with the integration of low carbon technology.

Process Fault Detection in Heat Recovery Steam Generator using an Artificial Neural Network Simplification of a Dynamic First Principles Model

Abstract A combined cycle power plant (CCPP) is a complex system with a Gas Turbine, Steam Turbine and a Heat Recovery Steam Generator (HRSG) working together. These three units work together and make the process highly interdependent. The onset of any fault in one of the above units would results in a significant reduction in overall efficiency and potentially lead to catastrophic accidents. Such failures can occur due to process faults because of large abrupt variations of operating conditions and structural faults due to corrosion, uneven stresses due to frequent cyclic operations. Conventionally, the identification of such leakage locations is made via visual inspection which is a time consuming and tedious. In the present work, we discuss a fault diagnosis strategy for an actual industrial HRSG present in a CCPP. Various steady state models at different loads of CCPP as well as a dynamic model are developed. Various structural faults in the form of leakages are incorporated in the heat exchangers. An Artificial Neural Network (ANN) model is developed based on data from the above simulations to detect the leaking heat exchangers.