Integrated condition-based planning of production and utility systems under uncertainty

A general rolling horizon optimization framework for the integrated condition-based operational and maintenance planning of production and utility systems in process industries is presented. In brief, the proposed optimization framework considers for the production and utility units: (i) improved unit performance degradation and recovery models that depend on both the cumulative time of operation and the unit operating levels deviation of units; (ii) modified operating capacities under online cleaning periods; (iii) different types of cleaning tasks (flexible time-window and online or offline condition-based); (iv) alternative options for offline cleaning tasks; (v) limited availability of cleaning resources; (vi) the initial state of the overall system at the beginning of each planning horizon; and (vii) terminal constraints for the rolling horizon problem. Total cost constitutes the objective function of the resulting problem and includes unit operating costs, cleaning costs, energy consumption costs and resource purchases costs. The case studies solved show that when compared to solutions obtained by sequential approaches the proposed integrated approach provides significantly better solutions in terms of total costs (reduction from 5%-32%), and especially in cost terms related to utility units operation, energy consumption, cleaning and startup/shutdown operations. Unnecessary cleanings and purchases of resources can be avoided by the proposed integrated approach. Overall, the significant reduction in total costs is a direct result of the enhanced energy efficiency of the overall system through the efficient generation and use of energy, the improved utilization of energy and material resources resulting in a more sustainable and cleaner production practices

A rolling horizon optimization framework for the simultaneous energy supply and demand planning in microgrids

This work focuses on the development of optimization-based scheduling strategies for the coordination of microgrids. The main novelty of this work is the simultaneous management of energy production and energy demand within a reactive scheduling approach to deal with the presence of uncertainty associated to production and consumption. Delays in the nominal energy demands are allowed under associated penalty costs to tackle flexible and fluctuating demand profiles. In this study, the basic microgrid structure consists of renewable energy systems (photovoltaic panels, wind turbines) and energy storage units. Consequently, a Mixed Integer Linear Programming (MILP) formulation is presented and used within a rolling horizon scheme that periodically updates input data information

A rolling horizon stochastic programming approach for the integrated planning of production and utility systems

This study focuses on the operational and resource-constrained condition-based cleaning planning problem of integrated production and utility systems under uncertainty. For the problem under consideration, a two-stage scenario-based stochastic programming model that follows a rolling horizon modeling representation is introduced; resulting in a hybrid reactive-proactive planning approach. In the stochastic programming model, all the binary variables related to the operational status (i.e., startup, operating, shutdown, under online or offline cleaning) of the production and utility units are considered as first-stage variables (i.e., scenario independent), and most of the remaining continuous variables are second-stage variables (i.e., scenario dependent). In addition, enhanced unit performance degradation and recovery models due to the cumulative operating level deviation and cumulative operating times are presented. Terminal constraints for minimum inventory levels for utilities and products as well as maximum unit performance degradation levels are also introduced. Two case studies are presented to highlight the applicability and the particular features of the proposed approach as an effective means of dealing with the sophisticated integrated planning problem considered in highly dynamic environments

A general optimization framework for the design and planning of energy supply chain networks: Techno-economic and environmental analysis

A general spatial optimization framework that relies on the use of a modified state-task network representation for design and planning problems in material and energy supply chain networks is presented. In brief, the proposed optimization framework considers for the tasks and states of the network: (i) the optimal selection and sizing of conversion, transfer and storage technologies, (ii) the capacity expansion for each technology over time, (iii) the inventory levels for storable states, (iv) the quantities of states converted or transferred through tasks, and (v) the optimal energy mix. Several variations of an illustrative design and planning problem of a mixed material and energy supply chain network have been solved effectively to study the trade-off between costs and emissions levels and different emissions regulation policies. A sensitivity analysis study with respect to alternative emissions caps and a multi-objective optimization example considering the conflicting objectives of total cost and emissions are also presented. The case studies showed that a more efficient way for emissions reductions is through regulation and emissions caps rather than increased emissions costs (i.e., 3.3% emissions reductions). Overall, the proposed optimization framework could be used to integrate various types of material and energy supply chain operations using a unified modeling representation towards the more efficient management of such interdependent networks under techno-economic and environmental aspects

Planning of production and utility systems under unit performance degradation and alternative resource-constrained cleaning policies

A general optimization framework for the simultaneous operational planning of utility and production systems is presented with the main purpose of reducing the energy needs and material resources utilization of the overall system. The proposed mathematical model focuses mainly on the utility system and considers for the utility units: (i) unit commitment constraints, (ii) performance degradation and recovery, (iii) different types of cleaning tasks (online or offline, and fixed or flexible time-window), (iv) alternative options for cleaning tasks in terms of associated durations, cleaning resources requirements and costs, and (v) constrained availability of resources for cleaning operations. The optimization function includes the operating costs for utility and production systems, cleaning costs for utility systems, and energy consumption costs. Several case studies are presented in order to highlight the applicability and the significant benefits of the proposed approach. In particular, in comparison with the traditional sequential planning approach for production and utility systems, the proposed integrated approach can achieve considerable reductions in startup/shutdown and cleaning costs, and most importantly in utilities purchases, as it is shown in one of the case studies

Solving scheduling problems in a multi-stage multi-product batch pharmaceutical industry

An iterative two-stage decomposition solution strategy for solving real-world scheduling problems in multi-stage multi-product batch plants is presented. The proposed method has as a core a mixed integer mathematical model, and consists of a constructive step, wherein a feasible and good solution is rapidly generated by following some insertion criteria, and an improvement step, wherein the initial solution is systematically enhanced by adopting several rescheduling techniques. The proposed strategy performance is tested on a number of problem instances of a complicated real-world multi-stage multi-product pharmaceuticals scheduling problem. High quality solutions are reported within reasonable computational time.Fil: Kopanos, Georgios. Universidad Politecnica de Catalunya; EspañaFil: Mendez, Carlos Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química (i); ArgentinaFil: Puigjaner, Luis. Universidad Politecnica de Catalunya; Españ

Optimization of a network of compressors in parallel: Operational and maintenance planning – The air separation plant case

A general mathematical framework for the optimization of compressors operations in air separation plants that considers operating constraints for compressors, several types of maintenance policies and managerial aspects is presented. The proposed approach can be used in a rolling horizon scheme. The operating status, the power consumption, the startup and the shutdown costs for compressors, the compressor-to-header assignments as well as the outlet mass flow rates for compressed air and distillation products are optimized under full demand satisfaction. The power consumption in the compressors is expressed by regression functions that have been derived using technical and historical data. Several case studies of an industrial air separation plant are solved. The results demonstrate that the simultaneous optimization of maintenance and operational tasks of the compressors favor the generation of better solutions in terms of total costs

Scheduling and lot-sizing in the dairy industry: the yoghurt production case

In this work, a continuous-time Mixed-Integer Linear Programming model (MILP) is developed for the short-term scheduling and lot-sizing problem in a multi-product yoghurt production line of a real-life dairy plant. The problem under question is mainly focused on the packaging stage considering though accurate timing and capacity constraints with respect to the fermentation stage. Packaging units are operating in parallel and share common resources. Sequence-dependent times and costs are explicitly taken into account and optimized by the proposed framework. Daily production line shut-down and setup times are also introduced, as a production policy to guarantee high quality of final products. To the best of our knowledge, the proposed approach is the first systematic attempt to explicitly address all the aforementioned issues in tandem. Several cases of a large-scale Greek dairy plant have been considered using the proposed model. Solutions obtained are presented, criticized and assessed in a real industrial environment. A number of benefits due to the use of optimization-based techniques are revealed. Finally, concluding remarks are drawn.Peer ReviewedPostprint (published version

Short-term scheduling in multi-stage batch plants through Lagrangean decomposition.

In this work, a continuous-time Mixed-Integer Linear Programming (MILP) model for the short-term scheduling in multi-stage batch plants is used. The MILP model accounts for ready unit times, release order times, sequence-dependent changeovers, transfer times between adjacent processing stages and different intermediates storage policies. A Lagrangean decomposition technique (Conejo et al., 2002) is applied to the MILP model in order to facilitate the resolution of real-world industrial cases. The proposed decomposition technique is thoroughly examined. An industrial case study of a multi-product multi-stage pharmaceuticals batch plant is addressed in order to demonstrate the performance and the advantages of the proposed decomposition scheme. The pharmaceutical plant under study consists of 17 processing equipments. The numerous (30 to 50) final products require 5 to 6 processing stages. Sequence-dependent changeovers are permitted in most stages. It is noteworthy that changeovers are usually of the same order of magnitude or even larger than the processing times. The main optimization goal is the minimization of the makespan. Results obtained are discussed highlighting the advantages and the special characteristics of the proposed scheduling model.Peer ReviewedPostprint (published version

A rolling horizon approach for optimal management of microgrids under stochastic uncertainty

This work presents a Mixed Integer Linear Programming (MILP) approach based on a combination of a rolling horizon and stochastic programming formulation. The objective of the proposed formulation is the optimal management of the supply and demand of energy and heat in microgrids under uncertainty, in order to minimise the operational cost. Delays in the starting time of energy demands are allowed within a predefined time windows to tackle flexible demand profiles. This approach uses a scenario-based stochastic programming formulation. These scenarios consider uncertainty in the wind speed forecast, the processing time of the energy tasks and the overall heat demand, to take into account all possible scenarios related to the generation and demand of energy and heat. Nevertheless, embracing all external scenarios associated with wind speed prediction makes their consideration computationally intractable. Thus, updating input information (e.g., wind speed forecast) is required to guarantee good quality and practical solutions. Hence, the two-stage stochastic MILP formulation is introduced into a rolling horizon approach that periodically updates input information