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Data-driven business process management-based development of Industry 4.0 solutions
Data-driven business process management-based development of Industry 4.0 solutions
Business process management (BPM) supports the management and transformation of organizational operations. This paper provides a structured guideline for improving data-based process development within the BPM life cycle. We show how Industry 4.0-induced tools and models can be integrated within the BPM life cycle to achieve more efficient process excellence and evidence-based decision-making. The paper demonstrates how standards of machine learning (CRISP-ML(Q)), BPM, and tools of design science research can support the redesign phases of Industry 4.0 development. The proposed methodology is carried out on an assembly company, where the proposed improvement steps are investigated by simulation and evaluated by relevant key performance indicators.
Multi-objective hierarchical clustering for tool assignment
Multi-objective hierarchical clustering for tool assignment
Due to the limited tool magazine capacities of CNC machines, time-consuming tool changeovers result in inefficient equipment utilization. This study provides a method to minimize the changeovers by optimizing the allocation of the tools to the machines. The proposed algorithm is efficient as it approaches the tool assignment task as a multi-objective hierarchical clustering problem where the products are grouped based on the similarity of the tool demands. The novelty of the goal-oriented agglomerative clustering algorithm is that it is based on the Pareto optimal selection of the merged clusters. The applicability of the method is demonstrated through an industrial case study. The tool assignment problem has also been formulated as a bin-packing optimization task, and the results of the related linear programming were used as a benchmark reference. The comparison highlighted that the proposed method provides a feasible solution for large real-life problems with low computation time.
Feeding trajectory optimization in fed-batch reactor with highly exothermic reactions
Feeding trajectory optimization in fed-batch reactor with highly exothermic reactions
Process engineers and operators should understand reactor runaway phenomenon to avoid hazard situations and accidents. Reactor runaway occurs when an exothermic reaction takes place in a reactor with insufficient heat transfer area. The reaction rate increases due to the temperature rise, causing a further increase in reaction rate and temperature. Without any safety action, only the reactant consumption limits the maximum temperature. One of the safety actions can be the application of runaway criteria to characterize the safe operating regimes where the optimal operating conditions can be found. This paper presents runaway criteria and the application of these in case of the feeding trajectory optimization of a fed-batch reactor model. The most important runaway criteria and the relations between them are presented, and critical curves of criteria are calculated using a simple dimensionless tubular reactor. The optimal feeding trajectory is determined in case a pilot plant fed-batch reactor using different runaway criteria as a non-linear constraint based on particle swarm optimization and sequential quadratic programming. Designers need to know how they can choose the right criterion, and the results can help with it. Selectivity and profit can be decreased if runaway occurs in a fed-batch reactor, therefore it is important to deal with this problem.
Dynamic Process Simulator Assisted Optimization of Operating Point Transition
Dynamic Process Simulator Assisted Optimization of Operating Point Transition
Model-based dynamic optimization is an effective tool for control and optimization of chemical processes, especially during transitions in operation. This study considers the dynamic optimization of load transitions. Poor operating point transition strategy can increase the quantity of off-spec product coming with financial loss, and also can increase the risk of malfunctions. A novel methodology for operating point transition optimization is applied to a vacuum distillation column unit in which concentration of cumene-hydroperoxide intermediate occurs. Optimization task is based on Open Platform Communication (OPC) between a commercial process simulator (Aspen HYSYS) and MATLAB. Nonlinear Optimization with Mesh Adaptive Direct Search algorithm (NOMAD) is applied to solve the task. Load of the distillation column is decreased from 100% to 90% taking into account the time of transition, amount of off-spec product and energy consumption. Different objective functions result definitely different transition strategies, therefore the right choice of this function is crucial step in this process. The results show that the proposed optimization methodology can be applied efficiently based on a complex simulator of the technology.
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