Medium-term optimization-based approach for the integration of production planning, scheduling and maintenance

A medium-term optimization-based approach is proposed for the integration of production planning, scheduling and maintenance. The problem presented in this work considers a multiproduct single-stage batch process plant with parallel units and limited resources. An MILP continuous-time formulation is developed based on the main ideas of travelling salesman problem and precedence-based constraints to deal with, sequence-dependent unit performance decay, flexible recovery operations, resource availability and product lifetime. Small scheduling examples have been solved and compared with adapted formulations from the literature, based on discrete-time and global-time events, demonstrating the effectiveness of the proposed solution approach. Additional planning and scheduling problems have been proposed by considering several time periods. Multi-period examples have been efficiently solved by the model showing the applicability of the solution approach for medium-size problems

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

Optimisation approaches for supply chain planning and scheduling under demand uncertainty

This work presents efficient MILP-based approaches for the planning and scheduling of multiproduct multistage continuous plants with sequence-dependent changeovers in a supply chain network under demand uncertainty and price elasticity of demand. This problem considers multiproduct plants, where several products must be produced and delivered to supply the distribution centres (DCs), while DCs are in charge of storing and delivering these products to the final markets to be sold. A hybrid discrete/continuous model is proposed for this problem by using the ideas of the Travelling Salesman Problem (TSP) and global precedence representation. In order to deal with the uncertainty, we proposed a Hierarchical Model Predictive Control (HMPC) approach for this particular problem. Despite of its efficiency, the final solution reported still could be far from the global optimum. Due to this, Local Search (LS) algorithms are developed to improve the solution of HMPC by rescheduling successive products in the current schedule. The effectiveness of the proposed solution techniques is demonstrated by solving a large-scale instance and comparing the solution with the original MPC and a classic Cutting Plane approach adapted for this work

Analysis of Fuel Reduction Strategies for Crude Distillation Unit

There is greater awareness today on the depleting fossil energy resources and the growing problem of atmospheric pollution. Engineers are developing practical techniques to ensure energy processes are designed and operated efficiently. Inefficient furnaces and heat exchangers contribute to the problem due to higher fuel demand and higher carbon emission. In crude preheat train (CPT), fouling causes the reduction of heat transfer efficiency, which leads to higher furnace fuel consumption, and exert additional cost for heat exchanger cleaning and lost production. This thesis presents strategies to reduce fuel consumption in the furnace, which will lead to reductions of operational cost and environmental emission. The method of exergy analysis is applied to determine the baseline energy efficiency of the furnace and CPT in a crude distillation unit (CDU). The strategies consist of locating and reducing exergy lost through process modifications of the energy system and developing optimum scheduling for retrofit and/or cleaning of heat exchangers in the CPT. There are two options for achieving fuel savings in the furnace. The options are reduction of heat lost from furnace stack and enhancement of heat recovery in the CPT. The second option involves plant shutdown for overall cleaning of CPT (Case 1), online cleaning of heat exchangers (Case 2) and combined online cleaning with retrofit of high efficiency heat exchangers (Case 3). Reduction of heat loss from furnace stack contributes to the smallest cost saving of 6.44% without carbon credit. With carbon credit, the saving is increased to 6.70%. The largest energy and carbon dioxide emission savings are found from Case 3. The installation of high efficiency heat exchangers improves furnace inlet temperature (FIT) from 215oC to 227oC. Furthermore, Case 3 results in the highest percentage of cost saving by about 71% and 62% with and without carbon credit, respectively. The payback period for investment in high efficiency heat exchangers is 3 months, with carbon credit, and 4 months, without carbon credit, respectively. Thus, Case 3 is the most cost effective option for reductions of energy consumption and carbon dioxide emission in the CDU

Optimization of hybrid dynamic/steady-state processes using process integration

Much research in the area of process integration has focused on steady-state processes. However, there are many process units that are inherently unsteady-state or perform best when operated in an unsteady-state manner. Unsteady-state units are vital to chemical processes but are unable to be included in current process optimization methods. Previous methods to optimize processes containing unsteady-state units place restrictions or constraints on their use. This optimization still does not give the best system design because the solution found will only be the best out of the available options which likely excludes the true optimal design. To remedy this, a methodology was created to incorporate unsteady-state process units into process optimization analysis. This methodology is as general as possible. Unlike many existing unsteadystate optimization methods, it determines all three main components of process design: the network configuration, sizes of units, and operation schedule. This generality ensures that the truly optimal process design will be found. Three problems were solved to illustrate the solution methodology. First, a general mass exchange network was optimized. The optimization formulation resulted in a mixed-integer nonlinear program, and linearization techniques were used to find the global solution. A property interception network was also optimized, the first work done using property integration for systems with unsteady-state behavior. Finally, an industrial semi-batch water purification system was optimized. This problem showed how process integration could be used to optimize a hybrid system and gain insights into the process under many different operating conditions

Supply chain management for the process industry

This thesis investigates some important problems in the supply chain management (SCM) for the process industry to fill the gap in the literature work, covering production planning and scheduling, production, distribution planning under uncertainty, multiobjective supply chain optimisation and water resources management in the water supply chain planning. To solve these problems, models and solution approaches are developed using mathematical programming, especially mixed-integer linear programming (MILP), techniques. First, the medium-term planning of continuous multiproduct plants with sequence-dependent changeovers is addressed. An MILP model is developed using Travelling Salesman Problem (TSP) classic formulation. A rolling horizon approach is also proposed for large instances. Compared with several literature models, the proposed models and approaches show significant computational advantage. Then, the short-term scheduling of batch multiproduct plants is considered. TSP-based formulation is adapted to model the sequence-dependent changeovers between product groups. An edible-oil deodoriser case study is investigated. Later, the proposed TSP-based formulation is incorporated into the supply chain planning with sequence-dependent changeovers and demand elasticity of price. Model predictive control (MPC) is applied to the production, distribution and inventory planning of supply chains under demand uncertainty. A multiobjective optimisation problem for the production, distribution and capacity planning of a global supply chain of agrochemicals is also addressed, considering cost, responsiveness and customer service level as objectives simultaneously. Both ε- constraint method and lexicographic minimax method are used to find the Pareto-optimal solutions Finally, the integrated water resources management in the water supply chain management is addressed, considering desalinated water, wastewater and reclaimed water, simultaneously. The optimal production, distribution and storage systems are determined by the proposed MILP model. Real cases of two Greek islands are studied

Thermo-Chemical Treatment (TCT) of Polymers in Multi-Scale Reactors: A Kinetics and Life Cycle Assessment (LCA) Study

The main reasons behind the success of the petrochemicals industry are not only the vast array of products that it provides - considered vital to our daily functions - but also the added value that it brings to the crude oil barrel price, making it a reliable venture for any concerned party. However, the industry is now faced with a fluctuating market and an unstable economy, which makes it imperative to find a more abundant and sustainable feedstock. Of all petrochemical derivatives, polymers (and their related industries) occupy the major share, and this makes the plastics industry a growing sector in terms of processing and conversion. Both virgin and waste plastics represent an attractive source of energy and product recovery. The main objective of this work was to investigate the thermo-chemical treatment (TCT) of polymers at different scales, and the reactors studied ranged from micro laboratory scale to industrial units suitable for covering large market demands. Within this framework, the degressive behaviour of polyolefin polymers (three virgin grades and two recyclate ones) was investigated alongside the products yielded (gases (C1-C4), liquids (non-aromatic C5-C10), aromatics (single ring structures) and waxes (> C11). This was achieved in a micro scale isothermal pyrolysis process, using 15 mg in a laboratory thermogravimetric analyser covering the temperature range of 500-600°C. The analysis led to the development of an nth order novel model on the basis of lumped products yielded by pyrolysis. The degradation mechanism was used to develop the mathematical breakdown of the primary, secondary and tertiary reactions. The model developed predicts the yield of the four different products and the polymer residual fraction at any operating condition proving to be a useful tool for reactor design and simulation, where the production of a specific chemical at a certain operating condition is paramount. In addition, laboratory scale isothermal pyrolysis experiments on end of life tyres (ELTs) were also conducted. This was achieved as a means to demonstrate the application of the concept previously applied to the polyolefins. A thermal cracking (degradation) scheme was proposed based on the global yielded products, which were lumped into four categories, namely gases (C1-C4), liquids (non-aromatic C5-C10), single ring aromatics (C5-C10), and char. The depolymerization kinetics (from primary, secondary and tertiary reactions) evaluation showed a high match with the experimental results obtained in this work. Finally, a life cycle assessment (LCA) was conducted for three integrated scenarios that reflect the current (2012) treatment of waste plastics in the Greater London area. The scenarios studied utilised a fraction of the polymers treated as a feedstock for two industrial scale TCT technologies; namely a low-temperature pyrolysis reactor that works using BP® technology and a hydrocracking unit that utilises the Veba-Combi Cracking (VCC®) concept. The scenarios studied also include transfer stations, a dry materials recovery facility (MRF) and a combined heat and power (CHP) incineration unit. The energy recovered via the different processes studied, as well as the chemicals and petrochemicals recovered, were all considered as credits in the LCA conducted. Chemicals obtained by the TCT units are very valuable and can replace refinery cuts and petrochemicals (e.g. syncrude (crude oil), naphtha, heavy (waxes) fraction (comparable to atmospheric residue), gases (C3 and C4) refinery cuts, etc.). This led to a technoeconomic analysis of the three integrated scenarios in order to assess the overall profitability. The analysis included capital, operating and maintenance costs, gate fees, transportation costs and corporation tax. The eligibility for governmental incentives (i.e. renewable obligation certificates (ROCs), levy exemption certificates (LECs) and packaging recovery notes (PRNs)) was also considered. The results obtained from the work carried out and reported in this thesis point towards ideal strategies for the treatment of polymers within the urban environment. It also provides a detailed understanding of potential products from polymers introduced to TCT units. This also aids the optimum recovery of petrochemicals, chemicals and energy from different TCT processes, and could help the UK Government in meeting its energy policy targets. It can also contribute to the energy security through diversification of supply. Finally, it provides a perspective on the integration between the crude oil upstream industry and different petrochemical complexes and oil refineries, through the use of different TCT units to increase the production of petrochemicals in existing plants

Operational and maintenance planning of production and utility systems in process industries.

Major process industries have installed onsite the utility systems that can generate several types of utilities for meeting the utility requirements of the main production systems. A traditional sequential approach is typically used for the planning of production and utility systems. However, this approach provides suboptimal solutions because the interconnected production and utility systems are not optimised simultaneously. In this research, a general optimisation framework for the simultaneous operational and maintenance planning of utility and production systems is presented with the main purpose of reducing the energy needs and resources utilisation of the overall system. A number of industrial-inspired case studies solved show that the solutions of the proposed integrated approach provides better solutions than the solutions obtained by the sequential approach. The results reported a reduction in total costs from 5% to 32%. The reduction in total costs demonstrate that the proposed integrated approach can result in efficient operation of utility systems by avoiding unnecessary purchases of utility resources and improved utilisation of energy and material resources. In addition, the proposed integrated optimisation-based model was further improved with the presence of process uncertainty in order to address dynamic production environment in process industries. However, integrated planning problems of production and utility systems results to large mixed integer programming (MIP) model that is difficult to solve to optimality and computationally expensive. With this regards, three-stage MIP-based decomposition strategy is proposed. The computational experiments showed that the solutions of the proposed MIP-based decomposition strategy can achieve optimal or near-optimal solutions at further reduced computational time by an average magnitude of 4. Overall, the proposed optimisation framework could be used to integrate production and utility systems for effective planning management in the realistic industrial scenarios.PhD in Energy and Powe