A Novel Back-Off Algorithm for the Integration Between Dynamic Optimization and Scheduling of Batch Processes Under Uncertainty

This thesis presents a decomposition algorithm for obtaining robust scheduling and control decisions. It iteratively solves scheduling and dynamic optimization problems while approximating stochastic uncertainty through back-off terms, calculated through dynamic simulations of the process. This algorithm is compared, both in solution quality and performance, against a fully-integrated MINLP

Integration of Scheduling and Control for Chemical Batch Plants under Stochastic Uncertainty: A Back-off Approach

Due to competitiveness in the chemical industrial sector, methodologies must be developed for an optimal programming of activities of a plant and a safe operation of its equipment. The formulation of a problem that involves the scheduling layer and control layer of the hierarchical manufacturing process results in a very complex model as each layer aims to satisfy different objectives and has specific constraints. Historically, in order to reduce the complexity of the model, each layer was considered as an individual problem, with assumptions that neglected phenomena that was observed in real processes. Thus, the solutions would seem economically attractive at first glance, but very difficult to implement in the practice due to a high level of information incoherence between the inter-phase variables. Consequently, methodologies that integrate the scheduling layer and the control layer need to be developed while considering aspects that may emerge during operation such as model uncertainty or plant-model mismatch. In this work, two back-off methodologies are presented to address Mixed Integer Dynamic Optimization (MIDO) formulations that arise when modeling the scheduling and control of flow-shop batch plants under stochastic parametric uncertainty. The core idea of the methodologies is to generate scheduling decisions, find control decisions and determine unit operation times that offer dynamic feasibility in the presence of stochastic parametric uncertainty. The MIDO problem is decomposed into a set of problems, with the aim to reduce the required computational time necessary to solve a full fletched MIDO formulation. The set of problems is then solved iteratively. The first methodology (Algorithm A) decomposes the MIDO problem into a scheduling problem, a dynamic optimization problem, a set of dynamic feasibility problems (dynamic feasibility test) and a unit time operation minimization problem. Since Algorithm A is limited by the sequential calculation of control decisions and unit operation times and that the scheduling does not accurately reflect the back-off dynamics of the system, a second algorithm (Algorithm B) was developed to address these issues. This algorithm decomposes the MIDO problem into a parametric sensitivity analysis, a scheduling problem, a dynamic optimization problem and a set of dynamic feasibility problems (dynamic feasibility test). The parametric sensitivity analysis is performed to create correlations that will allow the scheduling problem to consider the back-off dynamics of the system. Back-off terms are introduced in the model constraints of the dynamic optimization problem to represent the variability of the system caused by the uncertainty. Stochastic uncertainty is modeled using statistical distribution functions and are embedded in the set of dynamic feasibility problems to test the dynamic feasibility of the optimal control actions under random realizations in the uncertain parameters. The variability in the observed variables caused by the uncertain parameters while performing the dynamic feasibility test are used to calculate the back-off terms. To appreciate and evaluate the effect of the variations in the unit operations times caused by the back-off effect, in the scheduling problem, a continuous-time formulation has been considered and implemented. A case study featuring a flow-shop batch plant consisting of two dynamic reaction processes and two steady state separation processes is used to illustrate the benefits and limitations of the proposed back-off methodologies. An scenario consisting of a one unit available per process was use to compare Algorithm A with the methodology developed by Yael-Alvarez & Ricardez-Sandoval1. The results show that considering varying unit operation times in the back-off methodology increments the computational effort, but the economics of the are improved up to a 42%. Algorithm B was evaluated using scenarios to measure the effects of varying the value of variability considered in the back-off terms and the effects of having multiple available units with different processing capacities. The results show that the amount of variability considered in a back-off term may improve the profits up to a 22% per scheduled job compared to a nominal case (no back-off terms). Due to the stochastic nature of the uncertainty propagation models, only solutions that offer dynamic feasibility under uncertainty are assured. In general, unit operation times chosen from optimization are better suited to accommodate stochastic parametric uncertainty while the control actions enforce process operational and product quality constraints at reasonable economic costs. Hence, the two methods proposed in this work have the potential of addressing optimal scheduling and control problems under stochastic realizations in flow-shop batch plants

Methods and algorithms for integrated multi-scale optimisation of production planning and scheduling

Imperial Users onl

Bilişim paylaşımı ile gerçek zamanlı üretim planlama ve kontrol sistemi tasarımı

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Dijital teknolojilerin yaygınlaşması ve hayatın her alanına girmesi ihtiyaçların bireysel kapsamda ele alınmasını sağlamış, rekabeti kişiye özgü çözüm ve ürün üretme boyutuna taşımıştır. Buna bağlı olarak, üretim sistemlerinin gelişimi de çeşitliliği artırmaya ve yönetmeye yönelik olarak devam etmektedir. Bu gelişim ve dönüşüm süreci temel taşlarından birisi kitlesel özelleştime (mass - customization) olan dördüncü sanayi devrimi (Endüstri 4.0) olarak adlandırılmıştır. Dünyanın Endüstri 4.0'a ayak uydurabilmesi için üretim ortamında çeşitliliği ve çeşitliliğe bağlı olarak meydana gelecek değişkenliği yönetebilmesi gerekmektedir. Üretim ortamında, değişkenliğin yönetilebilmesi için geliştirilen yöntemler değişkenlikleri stok tutarak veya zaman toleransları ile çalışarak yönetmektedirler. Bu durum verimliliğin azalmasına ve birim başına düşen sabit maliyetin artmasına neden olmaktadır. Çalışmada, klasik yaklaşımların olumsuz yönlerinen arındırılmış bir üretim planlama yaklaşımı ve modeli önerilmiştir. Önerilen modelin değişkenliklerden etkilenmemesi için model değişken olan miktar parametresi yerine, değişkenliklerden daha az etkilenecek olan zaman parametresi üzerine kurulmuştur. Modelde stok seviyesi yerine stoğun tükenmesine kalan süreye dikkat edilmekte, çizelgeleme sürecinde de üretimin tamamlanmasına kalan süreye ve termin tarihine göre önceliklendirme yapılmaktadır. Model zaman hedeflerine bağlı çalığtığından gerçek zamanlı bir modeldir. Üretim modeli nin gerçek zamanlı olması değişkenliklerden, miktar tabanlı yaklaşıma göre, çok az etkilenmesini sağlamıştır. Yapılan kıyaslama çalışmalarıyla gerçek zamanlı planlama sisteminin üretim ortamındaki değişkenliklerden etkilenmediği ve emniyet stoksuz ortamda, gecikmeleri azaltarak üretimin tamamlanmasını sağladığı ortaya konmuştur. Üstelik bu çıktılar O(n) zaman karmaşıklığına sahip, kısa sürede, sonlanan algoritmalarla elde edilmiştir. Modelin uygulanması algoritmik olarak kolay olsa da, gerçek zamanlı olduğundan, gerçek zamanlı olarak belirlenen işlem döngüsü içerisinde güncel stok ve üretim verisine ihtiyaç duyulmaktadır. Bu veriler Endüstri 4.0 teknolojileriyle elde edilebilen veriler olduğundan, gerçek zamanlı üretim modeli modern üretim sistemlerinde uygulanabilir bir modeldir. Modelin üretim sistemine katkısı, sistemi aynı anda hem itme hem de çekme sistemi gibi çalıştırabilmesidir. Bu sayede üretim sistemi iki biçimde de çalışabilmektedir. Verimli olan stretejiye dinamik olarak geçmek de stok maliyetinin %90'dan fazla azalmasını sağlamıştır.Spread of digital technology in every slice of life provides that the needs have been addressed within the individual scope and also it increases competition to the level of both individual solution and personal production. Accordingly, the development of production systems continues to enhance for managing the diversity. One of the milestones of this development and transformation process is mass customization called the fourth industrial revolution, Industry4.0. Enterprises should be able to overcome with the diversity and variability due to diversity in the production environment in order to keep pace with Industry 4.0. The methods improved in attempt to cope with variability in the production, are keeping inventory or working with time tolerances. In this case, efficiency decreases and overhead cost per unit increases in. A novel production planning approach and a model which is eliminated from negative aspect of conventional methods has been proposed, in this study. The proposed model is based on a time parameter less affected by the variances rather than the quantity in order to avoid being influenced by the changes. The remaining time to stock-out instead of inventory level is taken into account in this model, and prioritization is proceed according to the time remaining to complete the production and due date in the scheduling process. Thus, the model based on a time parameter is a real-time model. Being real-time provides, the model, to be affected from variances less than quantity based methods. It is presented that the real-time model is not affected by the variances in the manufacturing environment, and provides completing manufacturing process with less delays by using no safety stock. Besides, an algorithm having O(n) time complexity provides this result. Though the application of model is easy as algorithmically, the model, being real-time, requires the live inventory and production data within the determined time cycle. Because the data can be gained by the cyber-physical technologies of Industry 4.0, real-time model can be applied to modern production systems. The contribution of this model to production systems is that the model assimilates manufacturing systems as pull or push system at the same time. Selecting the productive strategy dynamically enables the decrease of more than 90% inventory cost

Integration of Pumping Profile Design and Water Management Optimization for Shale Gas Production Systems

Unconventional shale gas production in the United States has been largely improved due to the development of hydraulic fracturing technology. However, the shale gas production system is generally complex; further, such enhanced levels of production have generated great concerns on its accompanying environmental implications, especially regarding shale gas water management. To handle the complexity associated with shale gas production system and identify the sustainable water management strategy, many optimization-based approaches have been developed. However, few of them considered the hydraulic fracturing operation as a dynamic process, where the pumping profile directly determines the volume of freshwater consumed and affects the production rates of both shale gas and wastewater. Considering the significant spatiotemporal variation in water footprint of hydraulic fracturing, those obtained planning and operational decisions of shale gas production system could be suboptimal and thus need to be updated when well development strategy changes. From another perspective, one problem could be that the pumping profile is generally designed to only maximize well productivity, without considering the impact of water management. To handle these challenges, the overall objective of this research is to develop a framework for the integration of pumping profile design and water management optimization to achieve the economically viable and environmentally sustainable water management strategy along with maximizing shale gas production. To this end, we initially focus on the development of a novel controller design framework for hydraulic fracturing while explicitly taking into account the associated post-fracturing water management. In particular, a dynamic input-output model is developed to estimate the characteristics of shale gas wastewater produced; and, a mapping-based technique is proposed to estimate the total annual cost of wastewater management and total revenue from shale gas. This framework is demonstrated to be capable to balance the trade-offs between hydraulic fracturing and water management by manipulating the pumping profile. Subsequently, we further extend this study by considering the following practical considerations. First, to better understand the significant spatiotemporal variation in water footprint associated with shale gas well development, the real water-use and flowback and produced (FP) water production data for individual shale gas wells drilled in the Eagle Ford and Marcellus shale regions are collected and analyzed. Herein, a typical model of shale gas production system is utilized to demonstrate how the variation in water recovery ratio can affect the optimal design and operation decisions. Second, to better describe the complex shale gas production system, an optimization model for shale gas supply chain network (SGSCN) incorporating of hydraulic fracturing water cycle is developed. Herein, capacity planning for both large-scale conventional facility and small-scale modular device is considered to achieve a flexible and efficient water management strategy. Third, to better integrate the optimization of shale gas production system and control of hydraulic fracturing, an online integrated scheduling and control framework with two feedback loops is proposed. Herein, the offset-free model predictive control (MPC) scheme is designed to compensate for plant-model mismatch