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As chemical plants become more flexible, the importance of batch processing has increased in recent years. Batch processes are also used in emerging areas such as semiconductor manufacturing. In order to derive the maximum benefit from batch processes, it is important that their operation be optimized. However, such optimization can be difficult since batch processes often involve complex, nonlinear phenomena. In this paper an approach to batch-to-batch optimization is coupled with neural network modeling to improve the performance of batch processes. The neural models yield results that are comparable to those achieved with first principle models. This accuracy is achieved through the use of feedback from each batch which effectively compensates for plant−model mismatch.

Abstract-Wormhole attack is a severe threat to wireless ad hoc and sensor networks. Most existing countermeasures either require specialized hardware devices or make strong assumptions on the network in order to capture the specific (partial) symptom induced by wormholes. Those requirements and assumptions limit the applicability of previous approaches. In this work, we present our attempt to understand the impact and inevitable symptom of wormholes and develop distributed detection methods by making as few restrictions and assumptions as possible. We fundamentally analyze the wormhole problem using a topology methodology, and propose an effective distributed approach, which relies solely on network connectivity information, without any requirements on special hardware devices or any rigorous assumptions on network properties. We rigorously prove the correctness of this design in continuous geometric domains and extend it into discrete domains. We evaluate its performance through extensive simulations. I. INTRODUCTIONWireless ad hoc and sensor networks are emerging as promising techniques for ubiquitous data exchange and information sharing. A particularly severe attack against wireless ad hoc and sensor networks is wormhole attack, which has been independently introduced in previous works [14] [9] [17]. In wormhole attacks, the attackers tunnel the packets between distant locations in the network through a highspeed out-of-band channel. The wormhole tunnel gives two distant nodes the illusion that they are close to each other. By building these wormhole tunnels, the attackers attract a large amount of network traffic and thus, are able to launch a variety of attacks, e.g., the attackers can selectively drop specified packets, forward packets out of order, modify packets, etc. More importantly, by collecting packets for analyzing traffic or compromising cryptographies, adversaries are able to use the wormhole attack as a stepping stone for many other more aggressive and severe attacks, such as network hijacking, man-in-the-middle attacks, and cipher breaking, significantly imperiling routing, localization, topology control, as well as many other network protocols [9]. Since the wormhole attack can be launched without compromising any legitimate node or cryptographic mechanisms [9], most generic security mechanisms are vulnerable to such attacks.The wormhole attack problem has received considerable attentions recently. Many countermeasures have been proposed to detect wormholes in wireless ad hoc and sensor networks. Those solutions typically catch the attacks by detecting