Helen H. Lou

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.

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.

Sustainability assessment of polygeneration processes based on syngas derived from coal and natural gas

Abstract Polygeneration systems produce chemicals, electricity, fuel, hydrogen, etc., from one or more type of feedstock. Considering their promise to provide high material and energy conversion from natural resources, polygeneration systems are recognized as promising technologies for future chemical and power industries. Sustainability assessment of these systems can provide valuable information to designers. Embedding exergy analysis and inherent safety score that quantify the efficiency and societal aspect, respectively, to complement the widely accepted economic assessment and environmental impacts assessment in a decision tree evaluation framework provides a more comprehensive, yet fast methodology to compare related processes in terms of sustainability. In this paper two different polygeneration systems, which use coal and natural gas as feed to produce di-methyl ether and power, are compared using a comprehensive sustainability assessment methodology. The results of the assessment are used to identify the more sustainable process, taking into account the economic, environmental, societal and efficiency factors.

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 Strategy for Multi-objective Optimization under Uncertainty in Chemical Process Design

Abstract In many circumstances, chemical process design can be formulated as a multi-objective optimization (MOO) problem. Examples include bi-objective optimization problems, where the economic objective is maximized and environmental impact is minimized simultaneously. Moreover, the random behavior in the process, property, market fluctuation, errors in model prediction and so on would affect the performance of a process. Therefore, it is essential to develop a MOO methodology under uncertainty. In this article, the authors propose a generic and systematic optimization methodology for chemical process design under uncertainty. It aims at identifying the optimal design from a number of candidates. The utility of this methodology is demonstrated by a case study based on the design of a condensate treatment unit in an ammonia plant.

Flare minimisation via dynamic simulation

This paper describes a project of flare minimisation during plant startup by using dynamic simulation. Dynamic simulation was developed for recovery area in an olefin plant and used to examine startup procedures: • approaching shutdown • startup with recycle ethane • starting the cracked feed and increasing the feed to normal production rate. The dynamic simulation gives an insight into the process dynamic behaviour that is not apparent through the use of steady state simulation. This information is crucial for plant startup in order to minimise the flaring. This project demonstrated a feasibility of pollution prevention through flare minimisation for an olefin plant.

Large-scale dynamic simulation for security assessment of an ethylene oxide manufacturing process

Abstract Safety is the second nature of chemical processes. Process security is the extended concept and practice of process safety. In large-scale chemical manufacturing, unit operations interact closely through various mass, energy, and momentum transfers. This may cause many possible occurrences of “chain-reaction” type disasters, which should be rooted out completely. To ensure process security and safety, the first step is the accurate assessment of security status of the processes. In this paper, a security-bearing, large-scale process dynamic modeling and simulation method is utilized to perform security assessment of an ethylene oxide production process involving various units, including a multi-tubular plug flow reactor operated under high pressure and temperature, adsorption and separation units, heat exchangers, recycle stream, and purge stream. The chemicals involved in this process possess serious environmental and health hazards. By simulating process behavior under various scenarios, this method can be used to classify the operational space, assess process security status quantitatively, foresee possible security failures, and give a critical review of design and operation policy to secure operation.

PROCESS SECURITY ASSESSMENT: OPERATIONAL SPACE CLASSIFICATION AND PROCESS SECURITY INDEX

Many chemical processes can be operationally risky, environmentally harmful and potentially dangerous when abnormal or destructive situations occur. This is due to the fact that chemical processes are often operated under high pressures, at high temperatures, and with fast material flows and complex manufacturing mechanisms. In the extreme, catastrophes such as explosions, toxic release and loss of life will occur unexpectedly and rapidly, particularly when a terrorist who has a sufficient technical background in chemical operations attacks a plant. To assure process security, systematic and effective process security-bearing operational strategies must be developed. This paper introduces a process operational space classification method and a process operational security index. The method and the index can be an effective quantitative tool for characterizing and analysing process security, especially when the process experiences various disturbances set by saboteurs who may be technically very knowledgeable. The efficacy of the quantification method is demonstrated by its application to an exothermic batch reactor.

Fuzzy-logic-based process modeling using limited experimental data

Abstract Process modeling with limited experimental data is always a difficult task. It becomes even more difficult if the process is highly nonlinear and is characterized by multiple inputs and outputs. Under these circumstances, fuzzy logic may show its capabilities for model development. In this paper, an efficient fuzzy modeling methodology is introduced. The resulting fuzzy model consists of a number of fuzzy implications, each of which is of an IF–THEN form. The IF part consists of a set of logically related antecedents, while the THEN part contains a consequent expressed as a set of linear models. To ensure model simplicity and to accelerate the modeling process, an effective model-development route has been developed. To guarantee the model’s reliability, a t -test-based non-linearity analysis is proposed when each fuzzy implication is developed. The efficacy of the methodology is demonstrated by modeling two nonlinear industrial processes.