Systematic generation of cyclic operating procedures based on timed automata

Manual synthesis of cyclic operating procedure in a realistic system is widely regarded as a difficult task since it is both time-consuming and error-prone. It is thus desirable to develop a systematic approach to automatically generate the optimal schedule of operation steps so as to achieve one or more specific production goal. The timed automata are utilized in the present work for such a purpose. In particular, all components in a given system and the corresponding control specifications are characterized with automata according to the proposed modeling rules. By using parallel composition, a system automaton can be produced with these models and the most appropriate operation path can then be identified accordingly. For any practical application, a sequential function chart and the corresponding Gantt chart can also be easily extracted from this path. Three examples are presented in this paper to demonstrate the feasibility of the proposed approach.     

Source-based discrete and continuous-time formulations for the crude oil pooling problem

The optimization of crude oil operations in refineries is a challenging scheduling problem due to the need to model tanks of varying composition with nonconvex bilinear terms, and complicating logistic constraints. Following recent work for multiperiod pooling problems of refined petroleum products, a source-based mixed-integer nonlinear programming formulation is proposed for discrete and continuous representations of time. Logistic constraints are modeled through Generalized Disjunctive Programming while a specialized algorithm featuring relaxations from multiparametric disaggregation handles the bilinear terms. Results over a set of test problems from the literature show that the discrete-time approach finds better solutions when minimizing cost (avoids source of bilinear terms). In contrast, solution quality is slightly better for the continuous-time formulation when maximizing gross margin. The results also show that the specialized global optimization algorithm can lead to lower optimality gaps for fixed CPU, but overall, the performance of commercial solvers BARON and GloMIQO are better.     

Scheduling dispensing and counting in secondary pharmaceutical manufacturing

In this article, we describe a general methodology for operations scheduling in dispensing and counting departments of pharmaceutical manufacturing plants. The departments are modeled as a multiobjective parallel machines scheduling problem under a number of both standard and realistic constraints, such as release times, due dates and deadlines, particular sequence‐dependent setup times, machine unavailabilities, and maximum campaign size. Main characteristics of the methodology are the modularity of the solution algorithms, the adaptability to different objectives and constraints to fulfill production requirements, the easiness of implementation, and the ability of incorporating human experience in the scheduling algorithms. Computational experience carried out on two case studies from a real pharmaceutical plant shows the effectiveness of this approach. © 2009 American Institute of Chemical Engineers AIChE J, 2009     

Integrated gasoline blending and order delivery operations: Part I. short‐term scheduling and global optimization for single and multi‐period operations

Gasoline is one of the most valuable products in an oil refinery and can account for as much as 60–70% of total profit. Optimal integrated scheduling of gasoline blending and order delivery operations can significantly increase profit by avoiding ship demurrage, improving customer satisfaction, minimizing quality give‐aways, reducing costly transitions and slop generation, exploiting low‐quality cuts, and reducing inventory costs. In this article, we first introduce a new unit‐specific event‐based continuous‐time formulation for the integrated treatment of recipes, blending, and scheduling of gasoline blending and order delivery operations. Many operational features are included such as nonidentical parallel blenders, constant blending rate, minimum blend length and amount, blender transition times, multipurpose product tanks, changeovers, and piecewise constant profiles for blend component qualities and feed rates. To address the nonconvexities arising from forcing constant blending rates during a run, we propose a hybrid global optimization approach incorporating a schedule adjustment procedure, iteratively via a mixed‐integer programming and nonlinear programming scheme, and a rigorous deterministic global optimization approach. The computational results demonstrate that our proposed formulation does improve the mixed‐integer linear programming relaxation of Li and Karimi, Ind. Eng. Chem. Res., 2011, 50, 9156–9174. All examples are solved to be 1%‐global optimality with modest computational effort. © 2016 American Institute of Chemical Engineers AIChE J, 62: 2043–2070, 2016     

Efficient multi-product multi-BOM batch scheduling for a petrochemical blending plant with a shared pipeline network

We present an effective scheduling heuristic for realistic production planning in a petrochemical blending plant. The considered model takes into account orders spanning a multi-product portfolio with multiple bills of materials per product, that need to be scheduled on shared production facilities including a complex pipeline network. Capacity constraints, intermediate storage restrictions, due dates, and the dedication of resources to specific product families have to be respected. The primary objective of the heuristic is to minimize the total order tardiness. Secondary objectives include the minimization of pipeline cleaning operations, the minimization of lead times, and the balanced utilization of filling units.The developed algorithm is based on a dynamic prioritization-based greedy search that schedules the orders sequentially. The proposed method can schedule short to mid-term operations and evaluate different plant configurations or production policies on a tactical level. We demonstrate its performance on various real-world inspired scenarios for different scheduling strategies.Our heuristic was used during the construction phase of a new blending plant and was instrumental in the optimal design of the plant.     

Optimal scheduling of continuous plants with energy constraints

This work addresses the scheduling of continuous single stage multiproduct plants with parallel units and shared storage tanks. Processing tasks are energy intensive and we consider time-dependent electricity pricing and availability together with multiple intermediate due dates, handled as hard constraints. A new discrete-time aggregate formulation is proposed to rapidly plan the production levels. It is combined with a continuous-time model for detailed scheduling as the essential part of a rolling-horizon algorithm. Their computational performance is compared to traditional discrete and continuous-time full-space formulations with all models relying on the Resource-Task Network (RTN) process representation. The results show that the new models and algorithm can generate global optimal schedules much more efficiently than their counterparts in problems involving unlimited power availability. Under restricted power, the aggregate model underestimates the electricity cost, which may cause the rolling-horizon approach to converge to a suboptimal solution, becoming the discrete-time model a better approach.     

从不规范设计书到系统逻辑控制器顺序功能流程图的设计

Today＇s automation industry is driven by the need for an increased productivity, higher flexibility, and higher individuality, and characterized by tailor-made and more complex control solutions. In the processing industry, logic controller design is often a manual, experience-based, and thus an error-prone procedure. Typically, the specifications are given by a set of informal requirements and a technical flowchart and both are used to be directly translated into the control code. This paper proposes a method in which the control program is constructed as a sequential function chart （SFC） by transforming the requirements via clearly defined intermediate formats. For the purpose of analysis, the resulting SFC can be translated algorithmically into timed automata. A rigorous verification can be used to determine whether all specifications are satisfied if a formal model of the plant is available which is then composed with the automata model of the logic controller （LC）.     

Cellular automata as models of inorganic structures self-assembly (Illustrated by uranyl selenate)

The theory of cellular automata is applied to describe the self-assembly of inorganic structures on molecular and nanoscale levels based on the example of uranyl selenates. The automaton that reproduces the structural topologies observed in these compounds is constructed, and its properties are studied. It is shown that the growth of complicated structural complexes in inorganic compounds depends on the structure of the nucleus as the initial condition of automaton’s work and, despite the topological differences of the resulting structures, the mechanisms of local interactions in these systems are identical. Under some conditions, this unity of mechanism leads to the formation of disordered structures.     

An automaton-based approach to evaluate and improve online diagnosis schemes for multi-failure scenarios in batch chemical processes

Online diagnosis has been considered as an important measure for improving operational safety in many batch chemical plants. Specifically, the state transition behaviors of all hardware items (components) in the given batch process and their failure mechanisms are modeled systematically with automata. The system model is then assembled by connecting the component models on the basis of a generic hierarchical structure. A “diagnoser” can be constructed accordingly for the purpose of determining various qualitative and quantitative performance indices. Guided by these indices, two performance enhancement approaches can be effectively applied: (1) installing additional sensors which are not included in the piping and instrumentation diagram (P&ID) and (2) executing extra operation steps which are not specified in the sequential function chart (SFC). Three examples are presented in this paper to demonstrate the feasibility of the proposed approach.     

Optimal planning and campaign scheduling of biopharmaceutical processes using a continuous-time formulation

This work addresses the optimal planning and campaign scheduling of biopharmaceutical manufacturing processes, considering multiple operational characteristics, such as the campaign schedule of batch and/or continuous process steps, multiple intermediate deliveries, sequence dependent changeovers operations, product storage restricted to shelf-life limitations, and the track-control of the production/campaign lots due to regulatory policies. A new mixed integer linear programing (MILP) model, based on a Resource Task Network (RTN) continuous time single-grid formulation, is developed to comprise the integration of all these features. The performance of the model features is discussed with the resolution of a set of industrial problems with different data sets and process layouts, demonstrating the wide application of the proposed formulation. It is also performed a comparison with a related literature model, showing the advantages of the continuous-time approach and the generality of our model for the optimal production management of biopharmaceutical processes.     

Mathematical modeling for simultaneous design of plants and supply chain in the batch process industry

Most supply chain design models have focused on the integration problem, where links among nodes must be settled in order to allow an efficient operation of the whole system. At this level, all the problem elements are modeled like black boxes, and the optimal solution determines the nodes allocation and their capacity, and links among nodes. In this work, a new approach is proposed where decisions about plant design are simultaneously made with operational and planning decisions on the supply chain. Thus, tradeoffs between the plant structure and the network design are assessed. The model considers unit duplications and the allocation of storage tanks for plant design. Using different sets of discrete sizes for batch units and tanks, a mixed integer linear programming model (MILP) is attained. The proposed formulation is compared with other non-integrated approaches in order to illustrate the advantages of the presented simultaneous approach.     

An MILP continuous-time approach to short-term scheduling of resource-constrained multistage flowshop batch facilities

This work presents a new MILP mathematical formulation for the resource-constrained short-term scheduling of flowshop batch facilities with a known topology and limited supplies of discrete resources. The processing structure is composed of multiple stages arranged in series and several units working in parallel at each one. All production orders consist of a single batch and follow the same processing sequence throughout the plant. The proposed MILP approach is based on a continuous time domain representation that relies on the notion of order predecessor and accounts for sequence-dependent setup times. Assignment and sequencing decisions are independently handled through separate sets of binary variables. A proper formulation of the sequencing constraints provides a substantial saving in sequencing variables and constraints. By postulating a pair of conditions for the simultaneous execution of processing tasks, rather simple resource constraints requiring a few extra binary variables are derived. The proposed MILP scheduling approach shows a remarkable computational efficiency when applied to real-world problems.     

Deterministic global flowsheet optimization: Between equation-oriented and sequential-modular methods

Flowsheet optimization problems often exhibit multiple (suboptimal) local solutions. However, deterministic global optimization can get prohibitively expensive with increasing flowsheet size and has been mostly restricted to equation-oriented flowsheet models. In this work, we demonstrate how a reduced-space formulation for global optimization (a hybrid between equation-oriented and sequential-modular methods) can be applied to flash calculations as well as a process flowsheet combining flash units, reaction, and recycling, and investigate the influence of model formulation on solution time. Both for flash units using ideal or nonideal thermodynamics as well as for the flowsheet, the reduced-space formulation can be solved over an order of magnitude faster than a fully equation-oriented formulation. This demonstrates the potential of the approach even for flowsheets with implicit unit operations. However, the results also highlight the importance of identifying suitable model formulations at the unit operation level as well as the flowsheet level. © 2018 American Institute of Chemical Engineers AIChE J, 65: 1022–1034, 2019     

Two stage stochastic bilevel programming model of a pre-established timberlands supply chain with biorefinery investment interests

This work studies the supply allocation problem, using a Stackelberg game, for an established timberlands supply chain with an additional decision of new biorefinery investments. In a timberlands system, harvester and manufacturer decision makers have separate objectives to maximize their respective profits. This interaction is represented with a turn based Stackelberg game. The harvesters decide first on the quantity harvested, and the manufacturers decide on how much to utilize. This game is modeled with a bilevel mathematical program. The novel feature of this paper's bilevel formulation is the inclusion of parametric uncertainty in a two stage model. The first stage problem involves logistical decisions around biorefinery investments, such as location and capacity, while the second stage problem involves a bilevel timberlands model with parameter uncertainty. Studying this problem formulation revealed interesting insights for solving multiperiod problems with bilevel stages as well as the decision maker's behavior for the timberlands model.     

New approach for scheduling crude oil operations

Scheduling of crude oil operations is crucial to petroleum refining, which includes determining the times and sequences of crude oil unloading, blending, and CDU feeding. In the last decades, many approaches have been proposed for solving this problem, but they either suffered from composition discrepancy [Lee et al. 1996. Mixed-integer linear programming model for refinery short-term scheduling of crude oil unloading with inventory management. Industrial and Engineering Chemistry Research 35, 1630-1641; Jia et al., 2003. Refinery short-term scheduling using continuous time formulation: crude-oil operations. Industrial and Engineering Chemistry Research 42, 3085-3097; Jia and Ierapetritou, 2004. Efficient short-term scheduling of refinery operations based on a continuous time formulation. Computer and Chemical Engineering 28, 1001-1019] or led to infeasible solutions for some cases [Reddy et al., 2004a. Novel solution approach for optimizing crude oil operations. A.I.Ch.E. Journal 50(6), 1177-1197; 2004b. A new continuous-time formulation for scheduling crude oil operations. Chemical Engineering Science 59, 1325-1341]. In this paper, coastal and marine-access refineries with simplified workflow are considered. Unlike existing approaches, the new approach can avoid composition discrepancy without using iterative algorithm and find better solution effectively. In this approach, a new mixed integer non-linear programming (MINLP) formulation is set up for crude oil scheduling firstly, and then some heuristic rules collected from expert experience are proposed to linearize bilinear terms and prefix some binary variables in the MINLP model. Thus, crude oil scheduling can be expressed as a complete mixed integer linear programming (MILP) model with fewer binary variables. To illustrate the advantage of the new approach, four typical examples are solved with three models. The new model is compared with the most effective models (RKS(a) and RKS(b) models) presented by Reddy et al. [2004a. Novel solution approach for optimizing crude oil operations. A.I.Ch.E. Journal 50(6), 1177-1197; 2004b. A new continuous-time formulation for scheduling crude oil operations. Chemical Engineering Science 59, 1325-1341], which proves that the new approach is valid and feasible in most small-size and medium-size problems.     

A global optimal formulation for the water integration in eco-industrial parks considering multiple pollutants

A mathematical programming formulation for the water integration in eco-industrial parks considering streams with several pollutants is presented. The formulation is based on a superstructure that allows the wastewater reuse in the same plant, the water exchange with different plants, and a shared set of interceptors that must be selected to determine the network configuration that satisfies process equipments and environmental constraints. The model formulation considers wastewater with several pollutants, and optimizes the network according to the minimum total annual cost, which includes the costs of fresh water, piping and regeneration. A new discretization approach is also proposed to handle the large set of bilinear terms that appear in the model in order to yield a near global optimal solution. The results obtained in several examples show considerable savings with respect to the solutions of the individual plant integration policy commonly employed for these types of problems.     

Application of neural networks to computer recipe prediction

Conventional mechanisms for computer colorant formulation commonly employ the Kubelka-Munk theory to relate reflectance values to colorant concentrations. However, there are situations where this approach is not applicable and hence an alternative is desirable. One such method is to utilize artificial intelligence techniques to mimic the behavior of the professional colorist. The purpose of this paper is to describe recent research being carried out at Reading University, sponsored by Courtaulds Research, that utilizes collections of cellular automata, known as neural networks, in the problem of recipe prediction.     

Application of partitioning and tearing techniques to sulfolane extraction plant

For effective flowsheet-level calculation of a complex chemical plant, the use of suitable partitioning and tearing techniques is inevitable. The partitioning and tearing techniques based on the reachability matrix and the decomposition algorithm were applied to model and simulate a sulfolane extraction plant. Basic concepts were first exploited towards a general representation for the modeling and simulation of the sulfolane extraction process. Six process units, which consist of the sulfolane extraction plant, were modeled first and partitioning and tearing techniques were employed to model and simulate the whole extraction plant. The Inside-Out method and the sequential-modular approach were used in the modeling and simulation. Results of simulations showed good agreement with plant operation data.     

A discrete crack approach to normal/shear cracking of concrete

This paper presents a numerical procedure for mixed mode fracture of quasi-brittle materials. The numerical procedure is based on the cohesive crack approach and extends it to mixed mode fracture. The crack path is obtained, and the mixed mode fracture model is incorporated into the crack path. The crack model is based on the formulation of the classical plasticity. The model is incorporated into a commercial finite element code by an user subroutine and is contrasted with experimental results. The numerical results agree quite well with two experimental sets of mixed mode fracture of concrete beams; one from Arrea and Ingraffea, the other from a nonproportional loading by the authors. Two other sets of experimental fracture results were modeled based on double-edge notched testing. The numerical procedure, mainly based on standard properties of the material measured by standard methods, predicts the experimental records of the load versus displacement at several control points of the specimens for three homothetic sizes of specimen.     

Cellular automata modeling of continuous stirred tank reactors

The classical dynamical systems model of continuous stirred tank reactors (CSTR) in which a first order chemical reaction takes place is reformulated in terms of the stochastic cellular automata procedure developed in the works of Seyborg [2] and Neuforth [3], which is extended by including the feed flow of chemical reactants. We show that this cellular automata model is able to simulate the dilution rate and the mixing process in the CSTR, as well as the details of the heat removal due to the jacket. The cellular automata approach is expected to be of considerable applicability at any industrial scale and especially for any type of microchemical system.