Fouling is a major problem in the operation of heat exchangers, resulting in increased capital, operational, and maintenance costs. Shell-and-tube heat exchangers are traditionally designed using fixed values of fouling resistances, ignoring that fouling rates depend on the exchanger geometry, rendering different fouling resistances for the same thermal service. This article discusses the use of fouling rate models in the design of shell-and-tube heat exchangers. We link a heat exchanger design algorithm to a dynamic simulation of the fouling rate. The proposed procedure is explored for the design of heat exchangers where fouling occurs in the tube side due to crude oil flow. Four examples illustrate how the utilization of the fouling rate model alters the solution of the design problem, including aspects related to a "no fouling" condition in the design, the impact of the duration of the operational campaign in the results, and how the uncertainty in the fouling prediction can be handled.

Industrial archived process data represent a convenient source of information for data-driven models, such as artificial neural network (ANN), that can be used for safety and efficiency improvement like early or even predictive fault detection and diagnosis (FDD). Nonetheless, most of the data used for model generation are representative of the process nominal states and therefore are not enough for classification problems intended to determine abnormal process conditions. This work proposes the use of techniques to augment the original real data standards, dismissing the need for experiments that could jeopardize process safety. It uses the Monte Carlo technique to artificially increase the number of model inputs coupled to the nearest neighbor search (NNS) by geometric distances to consistently classify the generated patterns in normal or faulty statuses. Finally, a radial basis function neural network is trained with the augmented data. The methodology was validated by a study case in which 3381 pulp and paper industrial data points were expanded to monitor the formation of particles in a recovery boiler. Only 5.8% of the original process data were examples of faulty conditions, but the new expanded and balanced data collection leveraged the classification performance of the neural network, allowing its future use for monitoring purpose.

Este trabalho apresenta uma revisão da literatura acerca dos níveis de automação da indústria de processos dos anos 1940 até os dias de hoje. Sua motivação é destacar o papel da automação de processos para fins de confiabilidade, segurança e eficiência das operações por meio de uma perspectiva histórica. Ele também contempla a definição das novas tecnologias digitais que operam sob a Indústria 4.0. Essas novas tecnologias incluem, sensores sem fio, análises do tipo Big Data, computação em nuvem, e internet das coisas. Aqui estão incluídos o estado da arte das tecnologias de automação vigentes e suas projeções de mercado. Palavras-chave: automação; instrumentação; estado da arte.This paper presents a literature review of the evolution of the levels of automation in process industry from the 1940s up until now. The motivation of this study is to evidence the role of process automation for reliability, safety and efficiency of operations from a historical perspective. It also includes technologies definitions that operate under Industry 4.0. Such new technologies include wireless sensors, Big Data analysis, cloud computation, and the internet of things. Here, the state of the art of the current automation technologies and its market projections are exposed.

Os sistemas de gerenciamento de dados são, atualmente, um dos cernes das indústrias químicas e bioquímicas. Este ambiente multidisciplinar demanda novas técnicas de aprendizado e ensino, tais como Problem Based Learning e sala de aula invertida. A Sala de Aula 4.0 é uma experiência de imersão industrial em um ambiente acadêmico, composto por uma caldeira semi-industrial com instrumentação digital integrada a um software gerenciador. O servidor, conectado à rede institucional, permite a análise de 150 variáveis de processo, aproximando o aluno do cenário de tomada de decisão em um ambiente industrial. A metodologia desenvolvida baseia-se em atividades de aprendizado teóricas e cognitivas para ampliar o aprendizado criativo, além de estimular o desenvolvimento de indicadores de produtividade (On Stream Factor e Overall Equipment Effectiveness) para quantificar KPIs subjetivos.

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