Yinlun Huang

On distributed fault-tolerant detection in wireless sensor networks

In this paper, we consider two important problems for distributed fault-tolerant detection in wireless sensor networks: 1) how to address both the noise-related measurement error and sensor fault simultaneously in fault-tolerant detection and 2) how to choose a proper neighborhood size n for a sensor node in fault correction such that the energy could be conserved. We propose a fault-tolerant detection scheme that explicitly introduces the sensor fault probability into the optimal event detection process. We mathematically show that the optimal detection error decreases exponentially with the increase of the neighborhood size. Experiments with both Bayesian and Neyman-Pearson approaches in simulated sensor networks demonstrate that the proposed algorithm is able to achieve better detection and better balance between detection accuracy and energy usage. Our work makes it possible to perform energy-efficient fault-tolerant detection in a wireless sensor network.In this paper, we consider two important problems for distributed fault-tolerant detection in wireless sensor networks: 1) how to address both the noise-related measurement error and sensor fault s...

Fuzzy model predictive control

A fuzzy model predictive control (FMPC) approach is introduced to design a control system for a highly nonlinear process. In this approach, a process system is described by a fuzzy convolution model that consists of a number of quasi-linear fuzzy implications. In controller design, prediction errors and control energy are minimized through a two-layered iterative optimization process. At the lower layer, optimal local control policies are identified to minimize prediction errors in each subsystem. A near optimum is then identified through coordinating the subsystems to reach an overall minimum prediction error at the upper layer. The two-layered computing scheme avoids extensive online nonlinear optimization and permits the design of a controller based on linear control theory. The efficacy of the FMPC approach is demonstrated through three examples.

Constructing a reliable neural network model for a plasma etching process using limited experimental data

Plasma etching has been widely used in the microelectronics industry to pattern submicro device geometries on silicon wafers. However, the fundamental plasma chemistry and physics in plasma etching reactors are not easy to model. Reliable empirical models for such a process are desirable for investigating the process behavior and realizing real-time control. One of the main difficulties encountered in this endeavour is that frequently very limited experimental data are available for model development for any particular apparatus. In the present work, a special artificial neural network (ANN) method is presented which shows how to develop satisfactory models even though fewer experimental data exist than there are coefficients in the ANN models. The method aims at constructing a model which can satisfy the criteria of minimum training error, maximum smoothness, and simplest network structure. Two ANN models were developed for a plasma etching reactor using CF/sub 4//O/sub 2/ or CF/sub 4//H/sub 2/ as a reactant that relate the manipulated and controlled variables or the manipulated and performance variables, respectively. Comparison of the predictions made by the ANNs with those made by the second order regression models that were used as the basis of the experimental design to get the data indicated that the ANNs predicted the process behavior more reasonably than the classical regression models when the process is operated at various operating conditions. >

Synthesis of an optimal wastewater reuse network

Process and manufacturing plants usually consume huge amounts of water in various cleaning and rinsing operations. Wastewater contains pollutants that are frequently environmentally regulated. An effective way to minimize wastewater is to design a wastewater reuse network (WWRN) such that the used water can be reused to a maximum extent in the same plant. In this paper, a mathematical approach is introduced to design an optimal network when multiple pollutants are contained in water streams. The approach is general, systematic, and easy to use. Its applicability is demonstrated by designing WWRNs for both papermaking and electroplating processes.

A sustainability root cause analysis methodology and its application

Abstract In the design of chemical/energy production systems, a major challenge is to identify the bottleneck issues and improve its sustainability effectively. Due to the multi-dimensional feature of sustainability, how to account for the impacts of various design factors and the cause-and-effect relationships can be very difficult. This paper will present a sustainability root cause analysis method based on the combination of Pareto Analysis and Fishbone diagram. The sustainability of the process is assessed incorporating economic, environmental, societal and efficiency concerns. This methodology is able to help the designers focus the attention on the most important fundamental causes, discover opportunities for sustainability improvement and provide critical guidance to design for sustainability. The efficacy of this methodology will be demonstrated through a case study on a biodiesel production technology.

Hierarchical decision making for proactive quality control: system development for defect reduction in automotive coating operations

Product quality control (QC) in manufacturing usually relies solely on inspection. Once a quality problem is found, a solution is sought usually based on experience, which is basically ad hoc. A new generation of QC requires the integration of both quality prediction and inspection. Automotive coating is a typical example. In the paint shop of an automotive assembly plant, topcoat filmbuild quality on vehicle surface has been a major concern. In production, defects are frequently generated in the very thin coating layers, which can degrade severely both coating appearance and durability. Trial and error in troubleshooting is a usual practice. In this paper, we introduce a proactive QC approach by resorting to artificial intelligence and engineering fundamentals. The approach is developed for solving a class of engineering problems for which conventional reactive QC approaches are feeble due to system complexity and uncertainties, such as that in paint applications. The main focus of the approach is on-process, rather than post-process. Thus, the domain knowledge about a process is fully explored and correlation of the process to product quality is established in a systematic way. In this approach the knowledge is expressed either symbolically or numerically, and structured in a hierarchy as reasoning progresses. Decision making is performed by a fuzzy MIN–MAX algorithm for heuristic knowledge and optimization for fundamental knowledge. To demonstrate the efficacy of the methodology, an application to QC of automotive topcoat is illustrated through developing an intelligent decision support system. This system is capable of evaluating process performance, and providing various valuable decision supports for defect prevention in different stages of a topcoat application process.

A fuzzy-logic-based approach to building efficient fuzzy rule-based expert systems

Abstract Knowledge-based expert systems have been increasingly applied to diverse engineering problems. These systems are mainly based on the compiled knowledge represented as production rules pertaining to the problem domains. In reality, the available information on any problem is almost always imprecise, incomplete and ill-defined; the linguistic variables need be defined as fuzzy variables which are mapped into appropriate numerical domains. Consequently, a fuzzy rule-based expert system (FRBES) is becoming attractive in problem solving. In such an expert system, however, a number of redundant rules as well as illogical or unnecessary interconnections among them frequently exist, thereby rendering the system unduly cumbersome and ineffective. In this paper, a fuzzy-logic-based approach is proposed for evaluating and simplifying the rule base of an FRBES. Fuzzy networks are generated from the rules in the original rule base, and a systematic and in-depth analysis of the resultant networks is conducted by means of fuzzy logic. Such an analysis gives rise to a new rule base derived from the original one; the former is always logically correct and structurally simpler than the latter. The efficacy of the proposed approach is demonstrated by applying it to the design of two FRBESs: one for cyanide waste minimization in an electroplating plant and the other for fault detection in the operation of hazardous waste incineration facilities.

A path constrained method for integration of process design and control

Integration of process design and control (IPDC) has been the holy grail of process systems engineering since the introduction of heat and mass integration. A proper combination of these separate yet connected tasks carries the promise of achieving superior designs that cannot be realized with conventional procedures. In this work, a bi-level dynamic optimization approach is introduced for achieving IPDC in its true sense. The principal idea proposed here is to utilize an optimal controller (a modified linear quadratic regulator) to practically evaluate the best achievable control performance for each candidate design during process design. The evaluation of complete, closed-loop system dynamics can then be meshed with a superstructure-based process design algorithm, thus enabling considering both cost and controllability in design of a process. The practicality of the introduced approach enables a solution of this complex dynamic optimization problem within reasonable computational requirements, as demonstrated in an evaporator case study.

Intelligent decision support for waste minimization in electroplating plants

Abstract Wastewater, spent solvent, spent process solutions, and sludge are the major waste streams generated in large volumes daily in electroplating plants. These waste streams can be significantly minimized through process modifiction and opertion improvement. In this endeavor, extensive knowledge covering various disciplines is required, which makes problem-solving extremely difficult. Moreover, available process data pertaining to waste minimization (WM) is usually inprecise, incomplete, and uncertain due to the lack of sensors, the difficulty of measurement, and process variations. These hinder the use of rigorous mathematical approaches in formulating WM problems. In the present work, an intelligent decision support system, namely WMEP-Advisor, is developed by resorting to artificial intelligence and fuzzy logic. This system is capable of performing detailed process analysis on waste-generation mechanisms, evaluating WM practice for an individual process unit or an entire plating process, identifying WM opportunities, and providing adequate decision support to process and environmental engineers for process modification and operational change. The tool can be used for either on-site WM or off-line personnel training.

ENVIRONMENTALLY CONSCIOUS HOIST SCHEDULING FOR ELECTROPLATING FACILITIES

ABSTRACT Hoist scheduling in electroplating operations has long been considered a key factor for improving the production rate. It has recently been recognized that hoist scheduling can also play an important role in waste minimization. In this work, a new hoist scheduling method is introduced for simultaneously achieving both the economic and environmental goals. A two-step dynamic optimization algorithm is introduced for identifying an optimal hoist schedule that can minimize the quantity and toxicity of wastewater streams from an electroplating line without loss of production rate. To improve computational efficiency, an engineering approach is adopted to reduce the number of binary decision variables in the optimization problem. An application to an actual electroplating process shows a significant reduction of both chemical and water consumption, which equates to a simultaneous realization of wastewater reduction and increase of profits.