Programming Model For Optimal Design Research Articles

A mixed integer nonlinear programming model for optimal design of natural gas storage surface double-pipe network

With the continuous increase of energy demand, the importance of natural gas storage has gradually increased. Underground natural gas storage (UNGS) has the characteristics of large capacity and low gas storage cost, and it is usually used as the main storage and peak shaving method for natural gas. As an important part of the surface engineering of UNGSs, the surface gathering and transportation system is responsible for gas injection and production and has a significant impact on the economic benefits of the entire surface engineering. Therefore, the optimization of the entire surface system has a great impact on improving economic benefits. However, there is no relevant article on the optimization of the dual-pipe injection-production pipeline network for the surface system of UNGSs. This paper considers the dual-pipe injection-production pipeline network of underground gas storage and establishes a Mixed-Integer Nonlinear Programming (MINLP) model. The goal of this model is to minimize the total cost of pipelines, platforms, and central stations. Constraints are considered in the model, such as the connection mode, facility/well number, length, flow balance, capacity, pressure, flow rate, pipe diameter, wall thickness, and variable value. The GAMS/IPOPT solution algorithm is adopted to find the best topology of the model, including the position of the platform and the central station, pipe diameter, wall thickness, and gas pressure and flow rate. Finally, two cases are used to compare the single and dual-pipe network systems to verify the effectiveness of the proposed model and the accuracy of the algorithm. The optimal results show that the mathematical model can reduce the total investment, and it can also guide the design and construction of the ground engineering of UNGSs.

A multi-year two-stage stochastic programming model for optimal design and operation of residential photovoltaic-battery systems

The residential PV-battery system is becoming economically viable due to technological advances. Selecting optimum investment portfolios and operation strategies are still the challenges faced by homeowners. This study proposed an integrated stochastic sizing-operating framework for optimal design and operation of grid-connected residential photovoltaic (PV)-battery systems. The two-stage stochastic programming method is adopted to address the uncertainties from load and PV productions, where 1) the first stage optimizes the investment portfolio of PV and battery, and 2) the second stage optimizes the energy system operation cost. A multi-year financial model is used to calculate the cash flows during the analysis period. The discounted net present values of expected net benefit (NB) are used as the maximization objective function to make optimal decisions for PV-battery investment and operation. Annual battery degradation factors estimate the aging battery effect. To separately evaluate the PV/battery profitability, we are the first to define the added value/cost metrics of PV/battery. Simulations based on realistic load/PV output profiles and estimated technical–economic parameters validated the effectiveness of the proposed framework and the added value/cost metrics of PV/battery components. The impacts of critical factors, including feed-in tariffs (FiT), tariff profiles and levels, and unit costs, were investigated to provide key findings to stakeholders. This framework can guide the homeowners to cost-effectively invest in and operate the distributed residential PV-battery system to facilitate the transition to a reliable, affordable, and clean building energy system.

Mixed-integer nonlinear programming models for optimal design of reliable chemical plants

Motivated by reliability/availability concerns in chemical plants, this paper proposes MINLP models to determine the optimal selection of parallel units considering the trade-off between availability and cost. Assuming an underlying serial structure for availability, we consider first a case where the system transitions between available and unavailable states, and second the case with an intermediate state at half capacity. Two non-convex MINLP models maximizing net profit are introduced for the two cases. In addition, a bi-criterion MINLP model is proposed to maximize availability and to minimize cost for the first case. It is shown that the corresponding epsilon-constrained model, where the availability is maximized subject to parametrically varying upper bound of the cost, can be reformulated as a convex MINLP. Availability is also incorporated in the superstructure optimization of process flowsheets. The performances of the proposed models are illustrated with a methanol synthesis and a toluene hydrodealkylation process.

Mathematical optimization for planning and design of cycle paths

The daily need for citizens to move for different activities, whatever its nature, has been greatly affected by the changes. The advantages resulting from the inclusion of the bicycle as a mode of transport and the proliferation of its use among citizens are numerous and extend both in the field of urban mobility and sustainable development.Currently, there are a number of programs for the implementation, promotion or increased public participation related to cycling in cities. But ultimately, each and every one of these initiatives have the same goal, to create a mesh of effective, useful and cycling trails that allow the use of bicycles in preferred routes with high guarantees of security, incorporating bicycle model intermodal urban transport.With the gradual implementation of bike lanes, many people have begun to use them to get around the city. But everything again needs a period of adaptation, and the reality is that the road network for these vehicles is full of obstacles to the rider. The current situation has led to the proposal that many kilometers of cycle paths needed to supply the demand of this mode of transport and, if implemented and planned are correct and sufficient.This paper presents a mathematical programming model for optimal design of a network for cyclists is presented. Specifically, the model determines a network of bicycle infrastructure, appropriate to the characteristics of a network of existing roads.As an application of the proposed model, the result of these experiments give a number of useful conclusions for planning and designing networks of cycle paths from a social perspective, applied to the case in the city of Malaga. DOI: http://dx.doi.org/10.4995/CIT2016.2016.4089

A fuzzy mixed‐integer linear programming model for optimal design of polygeneration systems with cyclic loads

Polygeneration plants are composed of integrated processes which produce multiple energy and material products simultaneously. Their inherent efficiency generally results in reduced emissions and environmental impacts compared to stand‐alone systems. However, rigorous mathematical modeling approaches need to be used for the synthesis of such systems. In this work, a fuzzy mixed‐integer linear programming (FMILP) model is proposed for the optimal design of a polygeneration plant with cyclic loads. A hypothetical but realistic polygeneration system case study is then solved to illustrate the applicability of the developed model. Results show that to accommodate varying cyclic loads, the optimal design of the polygeneration plant will require a storage tank. © 2016 American Institute of Chemical Engineers Environ Prog, 35: 1105–1112, 2016

Strategic planning design of microalgae biomass-to-biodiesel supply chain network: Multi-period deterministic model

Abstract Many studies have developed mathematical programming models for optimal design of supply chains for agricultural or lingocellulosic biomass-derived bioethanol production. However, because of the shortcomings of using agricultural (food supply problems) and lingo-cellulosic biomass (low biomass availability and processing yield) as feedstock, use of micro-algal biomass has been considered for use as a feedstock for biodiesel (biofuel). Thus, in this study we developed a deterministic mathematical programming model for strategic planning design of a microalgae biomass-to-biodiesel supply chain network (MBBSCN) from feedstock fields to end users that simultaneously satisfies resource constraints, demand constraints, and technology over a long-term planning horizon. The proposed deterministic model can help to determine where and how much feedstock to be transported, and where and how many refineries to be constructed to minimize the expected total cost including the co-product (naphtha and power) benefit. To demonstrate the feasibility of the proposed model, we conducted a case study based on the Korea biodiesel market data. In this case study, the optimized (i.e., most cost-effective) supply chain design can be gained at a reliable cost of ∼$US 5.91/gal ($US 1.56/l). In particular, this study can help to identify the technological bottlenecks and major cost drivers for the microalgae-to-diesel strategy, and can be also a guideline for development of various mathematical programming models for optimal design of microalgae biomass-derived biofuel supply chain like lingo-cellulosic biomass-based optimization studies.

A mixed integer programming model for optimal design of trigeneration in a hospital complex

A procedure for optimizing energy-management of a hospital complex is described, which, starting from hourly load diagrams, derived from the optimal design, allows the determination of the design and the running conditions of a trigeneration plant. The results obtained from the implementation of a mixed integer programming model, described here in detail, demonstrate how a trigeneration plant configuration utilizing heat pumps which are directly fed by a cogenerator, greatly improves energy-management, achieving significant economic, energy and environmental benefits.

Design of industrial packing/production processes with operational uncertainties

In this work, a mathematical programming model for optimal design of large scale packing/production systems in chemical and consumer goods manufacturing is presented. The model is capable of handling all possible deterministic sequence dependent changeover times and packing/production times as well as being sufficiently general to cope with uncertainties introduced by the probabilistic behaviour of operations, resource breakdowns, repair times and changeovers when such uncertainties can be defined mathematically. These uncertainties are often common in industry. This model is formulated in terms of random variables for packing/production times, packing/production volumes and the number of occurrence of downtimes. It is shown that the probabilistic model can be reduced to a mixed integer linear program (MILP), once the probability distribution functions for the uncertainties are defined. The model presented here, utilises an efficient ‘Continuous Time Formulation’ in which the timings of the activity events are mathematically defined relative to one another in a logical setting. The resulting model is solved to obtain a robust design which minimises an objective such as the variance of the production times of the packing/production systems. The advantage of this formulation is that it is possible to use a standard solver to solve large scale problems which makes the approach very attractive to industry.

An application of geometric programming to heat exchanger design

This paper presents a geometric programming model for optimal design of refrigeration heat exchangers. A dual method is used to solve the design problem to obtain the optimum values of the design variables. Analyses are performed to determine the changes in the optimum values of the design variables with respect to the changes in the unit costs of components, material specifications, and tube sizes.