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The introduced work aims to systematic optimize detailed short-term scheduling for industrial waste incineration to support daily decision-making processes. The overall objective is to reduce the utilization of auxiliary fuel used to overcome waste related energy in the incineration process. The reduction is achieved through adequate mixing and scheduling of waste streams with different energy content and improved logistic strategies. With it, regulatory and technical constraints are fulfilled and holdups in production processes derived from bottlenecks in the waste treatment are avoided. The considered system consists of storage tanks, a piping network, tank wagons for waste transportation, unloading pumps and incineration lances. The optimization model is formulated as a mixed-integer linear programming (MILP) problem with a discrete time representation based on a single uniform grid. It consists of two blocks: i) waste transfer and mixing, and ii) the incineration process itself including energy calculations and constraints. The industrial case study of this work shows a saving potential of auxiliary fuel of up to 90%, leading to a reduction of CO 2 emissions of up to 13%. Hence, the systematic scheduling of industrial waste incineration leads to an important reduction of auxiliary fuel consumption, resulting in economic and environmental benefits. [ABSTRACT FROM AUTHOR]

Optimizing the scheduling of liquid drug product manufacturing is paramount for pharmaceutical companies in their increasingly competitive environment and requires the modelling of industry-specific constraints. Such constraints include: (i) changing sequence-dependent setup times; (ii) maintaining a sterile production environment (e.g., through sterile holding times); (iii) periods with limited or no plant activity (e.g., no workforce during weekends); and (iv) demand timing (i.e., delivery deadline and release date constraints). In this work, an immediate precedence model is formulated to optimize the scheduling of liquid drug product manufacturing, considering the industry-specific constraints. The primary objective is to minimize the production makespan. Four case studies comprising up to 38 batches from a real multi-product facility illustrate the performance of the rigorous optimization approach. The makespan could be reduced by up to 7.9% compared to expert schedules. [ABSTRACT FROM AUTHOR]

Abstract: During batch process scheduling, products' batch size, processing conditions as well as operating times are usually established offline and considered out of the scope of the decision making stage. In practice, process dynamics may vary from the ones forecasted, in such a manner that the predicted optimal conditions will not be the best in practice. As a result of this mismatch, the plant usually operates under sub-optimal conditions, but if the process is flexible, its processing conditions can still be adapted to the actual plant needs in order to improve the overall performance. Given this situation, there is a strong motivation for developing models and optimization tools to fully integrate process dynamics into batch scheduling. In this work, the potential of directly including control variables with time varying values and variable batch sizes in the scheduling of batch plants is explored. The optimization of process dynamics, which is time varying, along with scheduling tasks is accomplished using rigorous mixed-integer dynamic optimization techniques. Through several examples, we show that integrating both decision-making levels can lead to significant economic savings. [Copyright &y& Elsevier]

Batch scheduling and control decision levels are usually optimized separately, even though integrated decisions would potentially increase the overall performance of the plant. This work aims at including in the scheduling problem the operating conditions related to the control function, which are traditionally optimized and fixed beforehand, to shed light to the benefits that may be obtained when using variable recipes in contrast to fixed nominal recipes. A multiproduct and a multipurpose plant illustrate the proposed approach. In both cases, the potential benefits are confirmed resulting in clear advantages regarding overall economic benefit, when the variable recipes are introduced. Thus, the presented approach gives rise to new tradeoffs, since it allows new improvement opportunities associated with quality, safety, or changing operating policies, which are usually considered separately. [ABSTRACT FROM AUTHOR]