Elisabet Capón-García

Scheduling and control decision-making under an integrated information environment

The complexity of decision-making in process industries and the need of highly competitive organizations require new supporting tools to coordinate and optimize the information flow among decision levels. This work presents a framework for integrating the scheduling and control decision levels by means of an ontology, which allows and coordinates the information exchange among the different modeling paradigms/conventions currently used for the enterprise-wide optimization (EWO). The scheduling of two multiproduct batch plants with increasing complexity is presented for illustrating the proposed working procedure.

Ontological framework for enterprise-wide integrated decision-making at operational level

In the domain of chemical process engineering, there is an increased interest in the integration of the enterprise hierarchical levels for decision-making purposes. At the scheduling level, decisions on the allocation of tasks to resources, sequencing and timing of tasks must be managed. However, such decisions are directly related to other enterprise actions, such as control and planning, but they are difficult to coordinate because they are modeled at different time and space scales, and their goals are not the same. In order to achieve integrated decisions supported by high quality information, there is a need to improve and develop robust computational tools and consistent models. In general, scheduling optimization approaches for decision-making differ depending on problem features, such as physical layout or time representation. Therefore, this work focuses on providing a framework based on a semantic model that captures the diversity in scheduling problem representation. Such semantic model uses the master recipe concept from the ANSI/ISA-88 standard perspective and encapsulates the scheduling decision task features. As a result, by the use of a single representation approach, any scheduling problem can be modeled and solved by its adequate optimization tool. The potential of a general model representation is presented by means of several case studies related to the scheduling function. Such case studies shed light to the model capabilities to represent different kinds and particular scheduling problems, achieving integration at the different decision support levels.

Intelligent Mathematical Modelling Agent for Supporting Decision-Making at Industry 4.0

The basis of decision-making at industry consists of formally representing the system and its subsystems in a model, which adequately captures those features that are necessary to reach consistent decisions. New trends in semantics and knowledge models aim to formalize the mathematical domain and mathematical models in order to provide bases for machine reasoning and artificial intelligence. Hence, tools for improving information sharing and communication have proved to be highly promising to support the integration of performance assessment within industrial decision-making. This work presents an intelligent agent based on knowledge models and establishes the basis for automating the design, management, programming and solution of mathematical models used in the industry. A case study concerning a capacity limitation constraint demonstrates the performance of the agent and indicates the directions for future work.

Operation scheduling of batch autothermal thermophilic aerobic digestion processes

Abstract The autothermal thermophilic aerobic digestion (ATAD) is an exothermic batchwise operated sludge treatment process, in which sludge undergoes heating and pasteurization as temperature attains values about 50–60xa0°C. Based on detailed process models, different approaches have been suggested (Rojas and Zhelev, 2009) to minimize the specific energy requirement of existing ATAD designs by altering the operating conditions, but less attention has been paid to process optimization by means of operation planning and timing. This work aims at minimizing the specific energy requirement of existing ATAD processes considering simultaneously operational conditions, planning and timing. Hence, a scheduling formulation including process model variables and a flexible recipe is proposed. Energy savings are reported for a case study of a simplified treatment plant. The proposed approach results in energy consumption reduction by optimizing batch sizes and operating conditions.