KPIs for Asset Management: A Pump Case Study

The integration of multiple data sources and the convergence of process control systems and business intelligence layer such as the enterprise resource planning (ERP) are paving the way for important progress in plant operation optimization. Numerous companies offer “Analytics Services” to leverage this newly available mine of data but applications still appear to be limited to certain specific types of large plants. Key Performance Indicators (KPIs) is arguably the most used approach to make sense of large and complex systems in a wide variety of fields and its applicability to industrial operations is more and more common, to the extent that standardization of KPIs has become a major topic for the International Organization for Standardization (ISO). While the KPI standard ISO 22400 focuses on KPIs for manufacturing operations management at the plant level, the scope of this thesis is to bring it to the first layer of the control system: the equipment. In addition to being part of the quest for operational excellence and energy efficiency, bringing KPIs to the asset level is an important step towards integration of the different layers of the automation pyramid, integrating in particular control and scheduling. Developed within the frame of the new generation of Operations Management Software for the process industries, this work presents a case study on the most widely used assets in the field – pumps, based on operational data of different plants in the Oil & Gas and Chemicals industries

Dispatcher3 D5.1 - Verification and validation plan

In this deliverable, we present a verification and validation plan designed to carry out all necessary activities along Dispatcher3 prototype development. Given the nature of the project, the deliverable points to a data-centric approach to machine learning that treats training and testing models as an important production asset, together with the algorithm and infrastructure used throughout the development. The verification and validation activities will be presented in the document. The proposed framework will support the incremental development of the prototype based on the principle of iterative development paradigm. The core of the verification and validation approach is structured around three different and inter-related phases including data acquisition and preparation, predictive model development and advisory generator model development which are combined iteratively and in close coordination with the experts from the consortium and the Advisory Board. For each individual phase, a set of verification and validation activities will be performed to maximise the benefits of Dispatcher3. Thus, the methodological framework proposed in this deliverable attempts to address the specificities of the verification and validation approach in the domain of machine learning, as it differs from the canonical approach which are typically based on standardised procedures, and in the domain of the final prospective model. This means that the verification and validation of the machine learning models will also be considered as a part of the model development, since the tailoring and enhancement of the model highly relies on the verification and validation results. The deliverable provides an approach on the definition of preliminary case studies that ensure the flexibility and tractability in their selection through different machine learning model development. The deliverable finally details the organisation and schedule of the internal and external meetings, workshops and dedicated activities along with the specification of the questionnaires, flow-type diagrams and other tool and platforms which aim to facilitate the validation assessments with special focus on the predictive and prospective models

Design, Implementation and Evaluation of Reinforcement Learning for an Adaptive Order Dispatching in Job Shop Manufacturing Systems

Modern production systems tend to have smaller batch sizes, a larger product variety and more complex material flow systems. Since a human oftentimes can no longer act in a sufficient manner as a decision maker under these circumstances, the demand for efficient and adaptive control systems is rising. This paper introduces a methodical approach as well as guideline for the design, implementation and evaluation of Reinforcement Learning (RL) algorithms for an adaptive order dispatching. Thereby, it addresses production engineers willing to apply RL. Moreover, a real-world use case shows the successful application of the method and remarkable results supporting real-time decision-making. These findings comprehensively illustrate and extend the knowledge on RL

Analytics and Intelligence for Smart Manufacturing

Digital transformation is one of the main aspects emerged by the current 4.0 revolution. It embraces the integration between the digital and physical environment,including the application of modelling and simulation techniques, visualization, and data analytics in order to manage the overall product life cycle

An optimization-based control strategy for energy efficiency of discrete manufacturing systems

In order to reduce the global energy consumption and avoid highest power peaks during operation of manufacturing systems, an optimization-based controller for selective switching on/off of peripheral devices in a test bench that emulates the energy consumption of a periodic system is proposed. First, energy consumption models for the test-bench devices are obtained based on data and subspace identification methods. Next, a control strategy is designed based on both optimization and receding horizon approach, considering the energy consumption models, operating constraints, and the real processes performed by peripheral devices. Thus, a control policy based on dynamical models of peripheral devices is proposed to reduce the energy consumption of the manufacturing systems without sacrificing the productivity. Afterward, the proposed strategy is validated in the test bench and comparing to a typical rule-based control scheme commonly used for these manufacturing systems. Based on the obtained results, reductions near 7% could be achieved allowing improvements in energy efficiency via minimization of the energy costs related to nominal power purchased.Peer ReviewedPostprint (author's final draft