Data-based process monitoring has become a key technology in process industries for safety, quality, and operation efficiency enhancement. This paper provides a timely update review on this topic. First, the natures of different industrial processes are revealed with their data characteristics analyzed. Second, detailed terminologies of the data-based process monitoring method are illustrated. Third, based on each of the main data characteristics that exhibits in the process, a corresponding problem is defined and illustrated, with review conducted with detailed discussions on connection and comparison of different monitoring methods. Finally, the relevant research perspectives and several promising issues are highlighted for future work.

Review of Recent Research on Data-Based Process Monitoring

High resolution schemes for hyperbolic conservation laws

A tutorial on linear and bilinear matrix inequalities

The lithium-ion battery is an ideal candidate for a wide variety of applications due to its high energy/power density and operating voltage. Some limitations of existing lithium-ion battery technology include underutilization, stress-induced material damage, capacity fade, and the potential for thermal runaway. This paper reviews efforts in the modeling and simulation of lithium-ion batteries and their use in the design of better batteries. Likely future directions in battery modeling and design including promising research opportunities are outlined.

/pdf/modeling-and-simulation-of-lithium-ion-batteries-from-a-5ggc923gnh.pdf

Modeling and Simulation of Lithium-Ion Batteries from a Systems Engineering Perspective

Review of adaptation mechanisms for data-driven soft sensors

Simultaneous design of discrete-time inverse response predictor control systems

In this paper, considering the multi-objective problems in batch processes, an improved multi-objective particle swarm optimization based on pareto-optimal solutions is proposed. In this method, a novel diversity preservation strategy that combines the information on distance and angle into similarity judgment is employed to select global best and thus guarantees the convergence and the diversity characteristics of the pareto front. As a result, enough pareto solutions are distributed evenly in the pareto front. Lastly, the algorithm is applied to a classical batch process. The results show that the quality at the end of each batch can approximate the desire value sufficiently and the input trajectory converges; thus verify the efficiency and practicability of the algorithm.

Multi-objective particle swarm optimization control technology and its application in batch processes

Adaptive 1MCDesignUsingMultiple Models

By making use of the multiple models to predict the process outputs in future time instants, a generalized predictive controller design is developed in this paper. However, the resulting nonlinear optimization problem cannot be solved straightforwardly because the values of the weighting functions in the prediction horizon are dependent on the future values of the process outputs and inputs. To circumvent this problem, an iterative procedure is developed. The proposed design is illustrated by considering the control of a polymerization reactor, in which an isothermal free-radical polymerization of methyl methacrylate is carried out using azo-bis-isobutyronitrile as initiator and toluene as solvent.

A multiple models based predictive control strategy applied in polymerization reactor control

Publisher Summary This chapter discusses the modeling methodology based on the framework of local model networks (LMN) to construct an accurate and simplified dynamic model of a pulse jet fabric filters (PJFF) PJFF sometimes called “bag house filters,” have become an attractive option of particulate collection utilities because of the feature that they can meet the stringent particulate emission limits regardless of variation in the operating conditions Other merits of pulse jet fabric filters are high collection efficiency, on-line cleaning applications, and outside collection that allows the bag maintenance in a clean and safe environment Despite the recent advances of LMN, a prior knowledge of the processes has to be exploited for determination of the LMN structure and the weighting functions The chapter describes extended self-organizing map (ESOM) network, which can overcome aforementioned difficulties, developed to construct the LMN using the input–output data Simulation results of a pulse jet fabric filter example show that the proposed method has better modeling accuracy as compared to both single global model and conventional LMN approach

An extended self-organizing map with application to the modeling of pulse jet fabric filters

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