Control Of Large-scale Industrial Processes Research Articles

Monitoring of the cellulosic ethanol fermentation process by near-infrared spectroscopy

Rapid, efficient, and low-cost technologies for monitoring the fermentation process during second generation (2G) or cellulosic ethanol production are essential for the successful implementation of this process at the commercial scale. Here, the use of near-infrared (NIR) spectroscopy associated with partial least squares (PLS) regression was investigated as a tool for monitoring the production of 2G ethanol from lignocellulosic sugarcane residues including bagasse, straw, and tops. The spectral data was based on a set of 103 alcoholic fermentation samples. Models based on different pre-processing techniques were evaluated. The best root mean square error of prediction (RMSEP) values obtained in the external validation were around 3.02g/L for ethanol and 6.60g/L for glucose. The findings showed that the PLS-NIR methodology was efficient in accurately predicting the glucose and ethanol concentrations during the production of 2G ethanol, demonstrating potential for use in monitoring and control of large-scale industrial processes.

Steady state hierarchical optimizing control for large-scale industrial processes with fuzzy parameters

This paper presents a new method for steady state hierarchical optimizing control of large-scale industrial processes. Several classical steady state coordination mechanisms are applied to the case in which the model coefficients of each subprocess of a large-scale industrial process are replaced by fuzzy numbers. Hence, each subprocess model is converted into a fuzzy form and then the original crisp programming problem with equality and inequality constraints is transformed into the fuzzy programming problem with fuzzy equality and crisp inequality constraints in each local decision unit. The final solutions are obtained by solving the general mathematical programming problem after the fuzzy equality constraints are converted into crisp inequality constraints. The developed method is mainly used to deal with the model-reality difference caused by either the model coefficients of the subprocess not being known accurately or the model slowly varying during normal operation. Three main types of coordination for processes with fuzzy parameters are derived in this paper: interaction balance method (IBM), interaction prediction method (IPM), and mixed method (MM). Simulation results of two examples show that 1) the proposed method can deal with model-reality difference efficiently, 2) the convergence speed of the on-line coordination for fuzzy parameter processes is faster than that of corresponding coordination for crisp parameter processes, and 3) the objective function of real processes can be improved by using the proposed method compared with the classical case. Furthermore, the studies show that the interaction balance method with global feedback (IBMF) based on a double iterative technique for processes with fuzzy parameters is the coordination algorithm that requires the fewest number of on-line iterations so far.

Intelligent control of wastewater treatment plants

In recent years intelligent control of large-scale industrial processes has brought about a revolution in the field of advanced control. Knowledge-based techniques which use linguistic rules elicited from human experts are at the core of this class of systems, of which fuzzy and neural controllers are two examples that have been applied with success. This paper presents an intelligent control system which can be embedded in a commercial programmable logic controller for the control of wastewater treatment plants.

104 An algorithm for stochastic steady-state optimizing control of large-scale industrial processes

104 An algorithm for stochastic steady-state optimizing control of large-scale industrial processes

Steady State Hierarchical Control of Large-scale Industrial Processes: A Survey

Abstract Large scale industrial processes are usually required to operate in a steady-state Manner. The development of hierarchical control of these processes is considered in three stages: static multilevel optimization, steady-state hierarchical optimization and integrated system optimization and parameter estimation. This paper surveys the main achievements in these three stages, and gives the perspective of future developments.

An Algorithm for Stochastic Steady-state Optimizing Control of Large-scale Industrial Processes

Abstract In this paper, the theory of stochastic steady-state optimizing control is proposed, and the steady-state concept of a stochastic industrial process is given, and a two level decomposition-coordination algorithm for optimizing control of stochastic steady-state large-scale industrial process is set up based on variance analysis and interaction balance method (IBM).The optimality and the convergence of the algorithm are proved. An example consisting of 3 subsystems is simulated and the results show that this algorithm is very efficient and reliable.

Optimal structures for steady-state adaptive optimizing control of large-scale industrial processes

Novel structures are developed and discussed for the steady-state optimizing control of large-scale industrial processes. A new class of hierarchical structures is proposed involving iterative procedures which utilize available process mathematical models and feedback information. In contrast to the majority of existing structures, the new techniques are optimal in the sense that the control produced by each structure satisfies the Kuhn-Tucker necessary optimality conditions. The structures are adaptive where the process model, which is assumed to be point-parametric, contains parameters which are updated at each iteration. Depending on the real process measurement capabilities, some of the structures incorporate output information feedback only, while others utilize both input and output measurements. These measurements are used in different ways leading to structures with local and global feedback, together with alternative coordination strategies.

Point measurements in the optimising control of large-scale industrial processes

A decentralised iterative scheme for the optimising control of large-scale systems is described which involves the use of a modified performance objective. Rather than using models or the process directly, operations on the modified objective index are performed at points generated by the iterative scheme.

A Research Facility for the Investigation of Computer Control of Large-Scale Industrial Processes

The implementation of multi-level control schemes for large industrial processes is discussed, with particular emphasis on the problems of data transmission between computers operating at various levels in the system. The operation of one type of inter-processor data link is considered in detail and its relevance as a research facility for hierarchical control systems is demonstrated. Applications which make use of this facility are presented illustrating the diverse data transmission requirements of multi-level systems.