An Optimisation-based Framework for Simultaneous Process Synthesis and Heat Integration

Simultaneous process synthesis, heat, power, and water integration of thermochemical hybrid biomass, coal, and natural gas facilities

Resource and energy efficiency are essential in process synthesis of chemical plants as they combine economic with ecological benefits. The two main targets of the process synthesis problem—mass and energy flux optimization—are typically split into two steps: single unit optimization and subsequent energy integration preventing the identification of the globally optimal solution. This article presents a single‐step procedure for resource‐efficient process synthesis through simultaneous heat and mass flux optimization called FluxMax approach (FMA), which is demonstrated for the production of hydrogen cyanide (HCN). The impact of simultaneous heat integration on the optimal process structure is demonstrated and two resource‐optimal processes for HCN production are identified consisting of a combination of different reactor and recycling strategies reducing total variable cost by 68%. For convex objective functions, the globally most resource‐efficient process is identified highlighting the potential of the FMA for site planning and retrofitting of existing plants.

Municipal solid waste to liquid transportation fuels – Part II: Process synthesis and global optimization strategies

Reprint of: Enhancing natural gas-to-liquids (GTL) processes through chemical looping for syngas production: Process synthesis and global optimization

Two new continuous-time MILP (Mixed Integer Linear Programming) models are developed for resource constrained project scheduling problems in which activities can be conducted in different (multi) modes. There are efficient and flexible MILP models for this problem in which time is represented by a number of discrete time intervals. However, they require to use large numbers of binary variables as the number of activities increases and time horizon stretches. Hence efforts have been made to formulate MILP models based on continuous-time representation and therefore to speed up the solution process. We propose two novel MILP models based on the event and resource flow concepts. The models are verified by solving benchmark problems in the literature.

MILP and MINLP models for the optimal scheduling of multi-energy systems accounting for delivery temperature of units, topology and non-isothermal mixing

Passive Optical Network (PON) deployments have recently been aiming to combine the capacity of metro and access networks in the last mile of the Internet service provisioning. Deployment of PONs by running fiber to the premises introduces the advantage of huge capacity but at the same time, it calls for a robust design in order to avoid long service outage durations in case of network failures where survivable network design is mostly limited to the deployment budget. In this paper, we propose three mixed integer linear programming (MILP) models for various survivability policies to deploy reliable long-reach PONs under the budget limitations. Each MILP model aims to place the ONUs in optimal locations so that the covered area is maximized while availability requirements of the users are satisfied within the deployment budget. We solve the MILP models under the uniform and heterogeneous availability requirement scenarios and show that service availability and coverage introduce a trade-off so as the coverage and deployment cost do. Two out of the three survivability policies can guarantee 99.99% service availability while the third one is able to guarantee 99.999% by running the proposed MILP models. However, the first two schemes are able to cover larger area when compared to the third scheme which is the most reliable protection policy.

Purpose: The main purpose of the paper is to evaluate the impact of diverse personnel policies around personnel promotion in the design of the strategic staff plan for a public university. The strategic staff planning consists in the determination of the size and composition of the workforce for an organization.Design/methodology/approach: The staff planning is solved using a Mixed Integer Linear Programming (MILP) model. The MILP model represents the organizational structure of the university, the personnel categories and capacity decisions, the demand requirements, the required service level and budget restrictions. All these aspects are translated into a set of data, as well as the parameters and constraints building up the mathematical model for optimization. The required data for the model is adopted from a Spanish public university.Findings: The development of appropriate policies for personnel promotion can effectively reduce the number of dismissals while proposing a transition towards different preferable workforce structures in the university.Research limitations/implications: The long term staff plan for the university is solved by the MILP model considering a time horizon of 8 years. For this time horizon, the required input data is derived from current data of the university. Different scenarios are proposed considering different temporal trends for input data, such as in demand and admissible promotional ratios for workers.Originality/value: The literature review reports a lack of formalized procedures for staff planning in universities taking into account, at the same time, the regulations on hiring, dismissals, promotions and the workforce heterogeneity, all considered to optimize workforce size and composition addressing not only an economic criteria, but also the required workforce expertise and the quality in the service offered. This paper adopts a formalized procedure developed by the authors in previous works, and exploits it to assess the impact of various personnel policies in the staff planning for a particular university case, and this is the principal contribution of the paper.

We propose a mixed-integer nonlinear programming (MINLP) model for simultaneous utility and heat exchanger area targeting with variable stream conditions. The model represents the composite-curve-based area targeting method by constructing the hot and cold composite curves mathematically. We introduce a “dynamic” enthalpy grid onto which the stream inlet/outlet temperatures and enthalpies are mapped. By calculating the temperatures at each grid point and the stream heat duties at each interval, the utility consumption and heat exchanger areas are simultaneously optimized using an economic criterion. We discuss preprocessing methods tailored to aid the solution of the proposed MINLP model. The model is applied to two illustrative examples as well as an example where it is integrated with a process synthesis model.

An H2 Polymer Electrolyte Membrane (PEM) Fuel Cell power generation system composed of three subsystems: fuel reforming, fuel cell stack, and post combustion is examined. The system is simulated and optimized with a fuel cell model integrated within the process flow sheet. A case study optimization of the entire power generation system is presented and the influence of heat integration on the optimization is demonstrated. Our objectives are to maximize the energy and system efficiencies as well as the system profit. Here, optimization formulations with heat integration are used to realize our objectives, which explore the synergy between optimization and heat integration. Our results showed that we can achieve an energy efficiency as high as 58.30% and a system efficiency as high as 28.35%. For comparison, we provide results obtained from sequential process optimization followed by heat integration. The gain from the synergy is mostly exemplified in the profit maximization study, where the profit increases by almost 27.15% in the best case. Finally, we obtain optimization results that include feed vaporization of water to steam as a comparison for the heat integration. These results demonstrate the importance of simultaneous optimal heat integration for fuel cell‐based processes. © 2006 American Institute of Chemical Engineers AIChE J, 2006

Mixed integer linear programming for the design of solar thermal energy systems with short-term storage

To support the selection of food items for FFQs in such a way that the amount of information on all relevant nutrients is maximised while the food list is as short as possible. Selection of the most informative food items to be included in FFQs was modelled as a Mixed Integer Linear Programming (MILP) model. The methodology was demonstrated for an FFQ with interest in energy, total protein, total fat, saturated fat, monounsaturated fat, polyunsaturated fat, total carbohydrates, mono- and disaccharides, dietary fibre and potassium. The food lists generated by the MILP model have good performance in terms of length, coverage and R 2 (explained variance) of all nutrients. MILP-generated food lists were 32-40 % shorter than a benchmark food list, whereas their quality in terms of R 2 was similar to that of the benchmark. The results suggest that the MILP model makes the selection process faster, more standardised and transparent, and is especially helpful in coping with multiple nutrients. The complexity of the method does not increase with increasing number of nutrients. The generated food lists appear either shorter or provide more information than a food list generated without the MILP model.

Nonlinear disjunctive programming models for the synthesis of heat integrated distillation sequences

Modeling side slopes in vertical alignment resource road construction using convex optimization

An improved MILP model for scheduling crude oil unloading, storage and processing