Data-based robust multiobjective optimization of interconnected processes: energy efficiency case study in papermaking.

Reducing energy consumption is a major challenge for "energy-intensive" industries such as papermaking. A commercially viable energy saving solution is to employ data-based optimization techniques to obtain a set of "optimized" operational settings that satisfy certain performance indices. The difficulties of this are: 1) the problems of this type are inherently multicriteria in the sense that improving one performance index might result in compromising the other important measures; 2) practical systems often exhibit unknown complex dynamics and several interconnections which make the modeling task difficult; and 3) as the models are acquired from the existing historical data, they are valid only locally and extrapolations incorporate risk of increasing process variability. To overcome these difficulties, this paper presents a new decision support system for robust multiobjective optimization of interconnected processes. The plant is first divided into serially connected units to model the process, product quality, energy consumption, and corresponding uncertainty measures. Then multiobjective gradient descent algorithm is used to solve the problem in line with user's preference information. Finally, the optimization results are visualized for analysis and decision making. In practice, if further iterations of the optimization algorithm are considered, validity of the local models must be checked prior to proceeding to further iterations. The method is implemented by a MATLAB-based interactive tool DataExplorer supporting a range of data analysis, modeling, and multiobjective optimization techniques. The proposed approach was tested in two U.K.-based commercial paper mills where the aim was reducing steam consumption and increasing productivity while maintaining the product quality by optimization of vacuum pressures in forming and press sections. The experimental results demonstrate the effectiveness of the method.

Thermal energy minimisation in papermaking via MD control approach

This paper reports the application of Advanced Process Control (APC) techniques for improving the thermal energy efficiency of a paperboard-making process by regulating the Machine Direction (MD) profile of the basis weight and moisture content of the paper-board. A Model Predictive Controller (MPC) is designed so that the sheet moisture and basis weight tracking errors along with variations of the sheet moisture and basis weight are reduced. Also, the drainage is maximised through improved wet-end stability which can facilitate driving the sheet moisture set-point closer to its upper specification limit over time. It is shown that the proposed strategy can result in reducing steam usage by 8-10%. A simulation study based on a UK board machine is presented to show the effectiveness of the proposed technique. © 2011 Intl Journal of Adv Mechatr.

Data-based robust multiobjective optimization of interconnected processes: Energy efficiency case study in papermaking

Reducing energy consumption is a major challenge for energy-intensive industries such as papermaking. A commercially viable energy saving solution is to employ data-based optimization techniques to obtain a set of optimized operational settings that satisfy certain performance indices. The difficulties of this are: 1) the problems of this type are inherently multicriteria in the sense that improving one performance index might result in compromising the other important measures; 2) practical systems often exhibit unknown complex dynamics and several interconnections which make the modeling task difficult; and 3) as the models are acquired from the existing historical data, they are valid only locally and extrapolations incorporate risk of increasing process variability. To overcome these difficulties, this paper presents a new decision support system for robust multiobjective optimization of interconnected processes. The plant is first divided into serially connected units to model the process, product quality, energy consumption, and corresponding uncertainty measures. Then multiobjective gradient descent algorithm is used to solve the problem in line with user's preference information. Finally, the optimization results are visualized for analysis and decision making. In practice, if further iterations of the optimization algorithm are considered, validity of the local models must be checked prior to proceeding to further iterations. The method is implemented by a MATLAB-based interactive tool DataExplorer supporting a range of data analysis, modeling, and multiobjective optimization techniques. The proposed approach was tested in two U.K.-based commercial paper mills where the aim was reducing steam consumption and increasing productivity while maintaining the product quality by optimization of vacuum pressures in forming and press sections. The experimental results demonstrate the effectiveness of the method. © 2006 IEEE.

Product scheduling for thermal energy reduction in papermaking industries

Papermaking is considered as an energy-intensive industry partly due to the fact that the machinery and procedures have been designed at the time when energy was both cheap and plentiful. A typical paper machine manufactures a variety of different products (grades) which impose variable per-unit raw material and energy costs to the mill. It is known that during a grade change operation the products are not market-worthy. Therefore, two different production regimes, i.e. steady state and grade transition can be recognised in papermaking practice. Among the costs associated with paper manufacture, the energy cost is 'more variable' due to (usually) day-to-day variations of the energy prices. Moreover, the production of a grade is often constrained by customer delivery time requirements. Given the above constraints and production modes, the product scheduling technique proposed in this paper aims at optimising the sequence of orders in a single machine so that the cost of production (mainly determined by the energy) is minimised. Simulation results obtained from a commercial board machine in the UK confirm the effectiveness of the proposed method. © 2011 IFAC.