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• A mathematical model is proposed for tissue paper drying process. • The model realizes the accurate simulation of key parameters in the drying process. • The model provides global optimization scheme for key operating parameters. • The model improves the competitiveness by reducing 8.72% of the drying cost. The drying process accounts for the largest proportion of energy consumption in paper mills. Energy system optimization has a great significance for reducing the energy consumption of the paper drying process. The drying process is a complex system that consists of several subsystems, such as cylinder and air hood systems. Previous optimization models for energy systems usually focused on these subsystems. A global optimization methodology for the entire drying process is lacking, and no existing models can be applied in practice. In this work, an energy system optimization model for the tissue paper drying process is proposed based on a process simulation model. The modeling process integrates the various subsystems and fully considers the coupling of the paper drying process, which greatly enhances the industrial application value of the model. Industrial operating data are used to test the simulation model, and the results show that the simulation error of each key variable is within 5%, which meets the real-world production requirements and lays the foundation for an energy efficiency analysis of each subsystem. Applying the optimization model to a tissue paper mill, the results show that it can reduce drying costs by 8.71%. [ABSTRACT FROM AUTHOR]

In recent years there has been increased concern on energy efficiency of industries. Since the scheduling problems in the shop floors are directly related to the energy consumption, an appropriate way to improve energy efficiency in industrial plants is to develop effective scheduling strategies. Hence, the aim of this paper is to design an energy-efficient scheduling in a shop floor industrial environment, i.e., flexible job-shop scheduling problem (FJSP). To this end, a multi-objective optimization model is developed with three objective functions: (i) minimizing total completion time, (ii) maximizing the total availability of the system, and (iii) minimizing total energy cost of both production and maintenance operations in the FJSP. To cope with this multi-objective optimization problem, an enhanced evolutionary algorithm incorporated with the global criterion, as a multi-objective handling technique, is proposed and then performance evaluation is performed based on an extensive numerical analysis. [ABSTRACT FROM AUTHOR]

Natural gas is usually transported by pipeline networks. This paper presents a multi-objective optimization model in optimizing the operation of natural gas pipeline networks. A mathematical model is established for different network configurations, namely linear, branch and looped topologies. Two conflicting objectives, power consumption minimization and gas delivery flow rate maximization, are considered subjected to pipeline and compressor constraints. The decision variables are the pressure at the nodes and the rotational speed of the compressors. A set of constraints is developed based on gas flow equations and compressors operating conditions. Due to the nonlinearity of the constraints and the objective, the developed model is a Nonlinear Programming problem. The optimization of the models is performed with NSGA-II algorithm. The solution obtained is a set of Pareto optimal points from which a decision maker can select a specific preferred solution. Sensitivity analysis was performed to determine the impact of parameter changes. [ABSTRACT FROM AUTHOR]

Vacuum/pressure swing adsorption is an attractive and often energy efficient separation process for some applications. However, there is often a trade-off between the different objectives: purity, recovery and power consumption. Identifying those trade-offs is possible through use of multi-objective optimisation methods but this is computationally challenging due to the size of the search space and the need for high fidelity simulations due to the inherently dynamic nature of the process. This paper presents the use of surrogate modelling to address the computational requirements of high fidelity simulations needed to evaluate alternative designs. We present SbNSGA-II ALM, surrogate based NSGA-II, a robust and fast multi-objective optimisation method based on kriging surrogate models and NSGA-II with the Active Learning MacKay (ALM) design criterion. The method is evaluated by application to an industrially relevant case study: a two column six step system for CO 2 /N 2 separation. A 5 times reduction in computational effort is observed. [ABSTRACT FROM AUTHOR]