Flexible Energy Management and Heat Exchanger Network Design

The design of heat exchanger networks (HEN) is a well-studied problem in process synthesis and an ideal test base to benchmark methods and techniques in the field. Despite a significant number of relevant publications, networks are still designed under assumptions of fixed operating conditions. Significant variations in supply and demand, alongside a need for efficient management in energy markets (energy grids, deregulated markets), impose limitations to this practice. Networks, designed with thermodynamic and economic efficiency under nominal operation, are known to have their efficiency dissipated and wasted in a context of similar though different conditions and demands. In a process plant, operational changes are common but designers still favor the staged approach of Pinch Technology (i.e., targeting-network development) where flexibility is not addressed properly and systematically. Alternatively, superstructure methods offer formulations with complexities hard to address by conventional algorithms. In this work, flexibility is addressed in a context amenable to targeting and network development stages, offering opportunities to visualise solutions and review options. For targeting, a dual approach is proposed that follows the framework of Hypertargets by Briones and Kokossis (1999a, 1999b, 1999c). The conceptual screening involves (i) the selection of cost-effective (primal) matches, and (ii) a model-based approach to assess the flexibility of the design options. Models and procedures are employed to assess trade-offs between operating cost (energy), capital cost (area), and the options' ability to handle variations (flexibility). Primal matches are automatically developed into network configurations with the use of mathematical models. A rigorous, superstructure-based approach is next applied to ensure the development of networks capable of handling operational variations without a need to consider exhaustive combinations of scenarios. The iterative approach incrementally augments the mathematical formulation by constraints and vertex conditions that guarantee consistency. The procedure is illustrated with two industrial problems and reports important improvements over conventional techniques.     

On the modelling of heat exchangers and heat exchanger network dynamics using bond graphs

ABSTRACT

Heat exchanger networks are important systems in most thermal engineering systems and are found in applications ranging from power plants and the process industry to domestic heating. Achieving cost-effective design of heat exchanger networks relies heavily on mathematical modelling and simulation-based design. Today, stationary design calculations are carried out for all new designs, but for some special applications, the transient response of complete networks has been researched. However, simulating large heat exchanger networks poses challenges due to computational speed and stiff initial value problems when flow equations are cast in differential algebraic form. In this article, a systems approach to heat exchanger and heat exchanger network modelling is suggested. The modelling approach aims at reducing the cost of system model development by producing modular and interchangeable models. The approach also aims at improving the capability for large and complex network simulation by suggesting an explicit formulation of the network flow problem.     

Synthesis of distillation trains with heat integration by a combined fuzzy and graphical approach

After passing some results of fuzzy synthesis of heat exchanger networks in review the application of this approach to the synthesis of distillation trains for the separation of multicomponent mixtures is demonstrated. The available heuristic rules are transformed into fuzzy implications dependent on different criteria and that competing rule, which is to be applied, is selected by the fuzzy algorithm.The theoretically possible amount of heat recovery within the networks generated in this way is estimated by a graphical method and included in the final cost evaluation. Regarding operating parameters like column pressure and reflux ratio, according to the mathematical effort at least qualitative statements can be made in result of the estimation of possible heat recovery.As already in the case of heat exchanger networks a following learning algorithm can serve to overcome the disadvantages of this sequential approach, representing at the same time the first step of a further evolutionary improvement of the initial distillation train.     

Controlled predatory pricing in a multiperiod Stackelberg game: an MPEC approach

We analyze a multiperiod oligopolistic market where each period is a Stackelberg game between a leader firm and multiple follower firms. The leader chooses his production level first, taking into account the reaction of the followers. Then, the follower firms decide their production levels after observing the leader’s decision. The difference between the proposed model and other models discussed in literature is that the leader firm has the power to force the follower firms out of business by preventing them from achieving a target sales level in a given time period. The leader firm has an incentive to lower the market prices possibly lower than the Stackelberg equilibrium in order to push the followers to sell less and eventually go out of business. Intentionally lowering the market prices to force competitors to fail is known as predatory pricing, and is illegal under antitrust laws since it negatively affects consumer welfare. In this work, we show that there exists a predatory pricing strategy where the market price is above the average cost and consumer welfare is preserved. We develop a mixed integer nonlinear problem (MINLP) that models the multiperiod Stackelberg game. The MINLP problem is transformed to a mixed integer linear problem (MILP) by using binary variables and piecewise linearization. A cutting plane algorithm is used to solve the resulting MILP. The results show that firms can engage in predatory pricing even if the average market price is forced to remain higher than the average cost. Furthermore, we show that in order to protect the consumers, antitrust laws can control predatory pricing by setting rules on consumer welfare.     

An analytical method for cost analysis in multi-stage supply chains: A stochastic network model approach

This paper studies the cost distribution characteristics in multi-stage supply chain networks. Based on the graphical evaluation and review technique, we propose a novel stochastic network mathematical model for cost distribution analysis in multi-stage supply chain networks. Further, to investigate the effects of cost components, including the procurement costs, inventory costs, shortage costs, production costs and transportation costs of supply chain members, on the total supply chain operation cost, we propose the concept of cost sensitivity and provide corresponding algorithms based on the proposed stochastic network model. Then the model is extended to analyze the cost performance of supply chain robustness under different order compensation ability scenarios and the corresponding algorithms are developed. Simulation experiment shows the effectiveness and flexibility of the proposed model, and also promotes a better understanding of the model approach and its managerial implications in cost management of supply chains.     

Designing Least-Cost Nonblocking Broadband Networks

Integrated network technologies, such as ATM, support multimedia applications with vastly different bandwidth needs, connection request rates, and holding patterns. Due to their high level of flexibility and communication rates approaching several gigabits per second, the classical network planning techniques, which rely heavily on statistical analysis, are less relevant to this new generation of networks. In this paper, we propose a new model for broadband networks and investigate the question of their optimal topology from a worst-case performance point of view. Our model is more flexible and realistic than others in the literature, and our worst-case bounds are among the first in this area. Our results include a proof of intractability for some simple versions of the network design problem and efficient approximation algorithms for designing nonblocking networks of provably small cost. More specifically, assuming some mild global traffic constraints, we show that a minimum-cost nonblockingstarnetwork achieves near-optimal cost; the cost ratio is at most 2 if switch source and sink capacities are symmetric and at most 3 when the total source and sink capacities are balanced. In the special case of unit link costs, we can show that a star network is indeed the cheapest nonblocking network.     

Static target search path planning optimization with heterogeneous agents

Disruptions in airline operations can result in infeasibilities in aircraft and passenger schedules. Airlines typically recover aircraft schedules and disruptions in passenger itineraries sequentially. However, passengers are severely affected by disruptions and recovery decisions. In this paper, we present a mathematical formulation for the integrated aircraft and passenger recovery problem that considers aircraft and passenger related costs simultaneously. Using the superimposition of aircraft and passenger itinerary networks, passengers are explicitly modeled in order to use realistic passenger related costs. In addition to the common routing recovery actions, we integrate several passenger recovery actions and cruise speed control in our solution approach. Cruise speed control is a very beneficial action for mitigating delays. On the other hand, it adds complexity to the problem due to the nonlinearity in fuel cost function. The problem is formulated as a mixed integer nonlinear programming (MINLP) model. We show that the problem can be reformulated as conic quadratic mixed integer programming (CQMIP) problem which can be solved with commercial optimization software such as IBM ILOG CPLEX. Our computational experiments have shown that we could handle several simultaneous disruptions optimally on a four-hub network of a major U.S. airline within less than a minute on the average. We conclude that proposed approach is able to find optimal tradeoff between operating and passenger-related costs in real time.     

Sequential location-allocation problems on chains and trees with probabilistic link demands

In this paper, we consider multiperiod minisum location problems on networks in which demands can occur continuously on links according to a uniform probability density function. In addition, demands may change dynamically over time periods and at most one facility can be located per time period. Two types of networks are considered in conjunction with three behavioral strategies. The first type of network discussed is a chain graph. A myopic strategy and long-range strategy for locatingp-facilities are considered, as is a discounted present worth strategy for locating two facilities. Although these problems are generally nonconvex, effective methods are developed to readily identify all local and global minima. This analysis forms the basis for similar problems on tree graphs. In particular, we construct algorithms for the 3-facility myopic problem and the 2-facility long-range and discounted cost problems on a tree graph. Extensions and suggestions for further research on problems involving more general networks are provided.     

Network simplex algorithm for the general equal flow problem

In this paper the general equal flow problem is considered. This is a minimum cost network flow problem with additional side constraints requiring the flow of arcs in some given sets of arcs to take on the same value. This model can be applied to approach water resource system management problems or multiperiod logistic problems in general involving policy restrictions which require some arcs to carry the same amount of flow through the given study period. Although the bases of the general equal flow problem are no longer spanning trees, it is possible to recognize a similar structure that allows us to take advantage of the practical computational capabilities of network models. After characterizing the bases of the problem as good (r+1)-forests, a simplex primal algorithm is developed that exploits the network structure of the problem and requires only slight modifications of the well-known network simplex algorithm.     

A fuzzy programming approach for dynamic virtual hub location problem

Hub location problem has been used in transportation network to exploit economies of scale. For example, a controversial issue in the planning of air transportation networks is inclement weather or emergency conditions. In this situation, hub facilities would not be able to provide a good service to their spoke nodes temporarily. Thus, some other kinds of predetermined underutilized facilities in the network are used as virtual hubs to host some or all connections of original hubs to recover the incurred incapacitation and increase network flexibility and demand flow. In such an unexpected situation, it is not unreasonable to expect that some information be imprecise or vague. To deal with this issue, fuzzy concept is used to pose a more realistic problem. Here, we present a fuzzy integer liner programming approach to propose a dynamic virtual hub location problem with the aim of minimizing transportation cost in the network. We examine the effectiveness of our model using the well-known CAB data set.     

The multiperiod assignment problem: A multicommodity network flow model and specialized branch and bound algorithm

The multiperiod assignment problem, a specialization of the three dimensional assignment problem, is an optimization model that describes the situation of assigning people to activities, or jobs over time. We consider the most general case which has both a cost of assigning a person to an activity in each time period, and a cost of transferring the person from one activity in each period to another activity in the next period. In general, the number of time periods need not equal the number of persons and activities. We present a new formulation of the multiperiod assignment problem, that of an integer, multicommodity network flow model. The special structure of the model allows us to find a good feasible solution relatively quickly by a shortest path heuristic algorithm. We discuss a new branch and bound algorithm for solving this problem to optimality. The subproblems of the branch and bound tree are evaluated by solving a set of special-structured, shortest path problems all of which can be solved in order n²T time, where n is the number of persons and activities, and T is the number of time periods. We present computational tests of the algorithm on moderately sized problems.     

Combinatorial flexibility problems and their computational complexity

The concept of flexibility—originated in the context of heat exchanger networks—is associated with a substructure which guarantees the performance of the original structure, in a given range of possible states. We extend this concept to combinatorial optimization problems, and prove several computational complexity results in this new framework.Under some monotonicity conditions, we prove that a combinatorial optimization problem polynomially transforms to its associated flexibility problem, but that the converse need not be true.In order to obtain polynomial flexibility problems, we have to restrict ourselves to combinatorial optimization problems on matroids. We also prove that, when relaxing in different ways the matroid structure, the flexibility problems become NP-complete. This fact is shown by proving the NP-completeness of the flexibility problems associated with the Shortest Path, Minimum Cut and Weighted Matching problems.     

Increasing flexibility and productivity in Industry 4.0 production networks with autonomous mobile robots and smart intralogistics

Manufacturing flexibility improves a firm’s ability to react in timely manner to customer demands and to increase production system productivity without incurring excessive costs and expending an excessive amount of resources. The emerging technologies in the Industry 4.0 era, such as cloud operations or industrial Artificial Intelligence, allow for new flexible production systems. We develop and test an analytical model for a throughput analysis and use it to reveal the conditions under which the autonomous mobile robots (AMR)-based flexible production networks are more advantageous as compared to the traditional production lines. Using a circular loop among workstations and inter-operational buffers, our model allows congestion to be avoided by utilizing multiple crosses and analyzing both the flow and the load/unload phases. The sensitivity analysis shows that the cost of the AMRs and the number of shifts are the key factors in improving flexibility and productivity. The outcomes of this research promote a deeper understanding of the role of AMRs in Industry 4.0-based production networks and can be utilized by production planners to determine optimal configurations and the associated performance impact of the AMR-based production networks in as compared to the traditionally balanced lines. This study supports the decision-makers in how the AMR in production systems in process industry can improve manufacturing performance in terms of productivity, flexibility, and costs. 

     

Network synthesis under survivability constraints

Telecommunication networks are subject to link and equipment failures. Since failures cannot be entirely avoided, networks have to be designed so as to survive failure situations. In this paper, we are interested in designing low cost survivable networks. Given point-to-point traffic demands and a cost/capacity function for each link, we aim at finding the minimum cost capacities satisfying the given demands and survivability requirements. A survivability model that reroutes interrupted traffic using all the available capacities on the network is presented and studied. In the proposed model, capacity and flow assignments for each network operating state are jointly optimized. We prove the -hardness of the optimisation problem defined by dual constraints. Then, we propose a polynomial relaxation along with a fast heuristic to compute a feasible solution of the problem from its relaxed optimal solution. Our solution approaches are tested on a set of problem instances.Received: September 2002, Revised: July 2003, AMS classification: 90C05     

Architectures,stability and optimization for clock distribution networks

Synchronous telecommunication networks, distributed control systems and integrated circuits have its accuracy of operation dependent on the existence of a reliable time basis signal extracted from the line data stream and acquirable to each node. In this sense, the existence of a sub-network (inside the main network) dedicated to the distribution of the clock signals is crucially important. There are different solutions for the architecture of the time distribution sub-network and choosing one of them depends on cost, precision, reliability and operational security. In this work we expose: (i) the possible time distribution networks and their usual topologies and arrangements. (ii) How parameters of the network nodes can affect the reachability and stability of the synchronous state of a network. (iii) Optimizations methods for synchronous networks which can provide low cost architectures with operational precision, reliability and security.     

Multi-objective optimization of heat exchangers using a modified teaching-learning-based optimization algorithm

Teaching-learning-based optimization (TLBO) is a recently developed heuristic algorithm based on the natural phenomenon of teaching-learning process. In the present work, a modified version of the TLBO algorithm is introduced and applied for the multi-objective optimization of heat exchangers. Plate-fin heat exchanger and shell and tube heat exchanger are considered for the optimization. Maximization of heat exchanger effectiveness and minimization of total cost of the exchanger are considered as the objective functions. Two examples are presented to demonstrate the effectiveness and accuracy of the proposed algorithm. The results of optimization using the modified TLBO are validated by comparing with those obtained by using the genetic algorithm (GA).     

Designing two-echelon supply networks

A modern distribution network design model needs to deal with the trade-offs between a variety of factors, including (1) location and associated (fixed) operating cost of distribution centers (DCs), (2) total transportation costs, and (3) storage holding and replenishment costs at DCs and retail outlets. In addition, network design models should account for factors such as (4) stockouts, by setting appropriate levels of safety stocks, or (5) capacity concerns, which may affect operating costs in the form of congestion costs. The difficulty of making such trade-offs is compounded by the fact that even finding the optimal two-echelon inventory policy in a fixed and uncapacitated distribution network is already a hard problem. In this paper, we propose a generic modeling framework to address these issues that continues and extends a recent stream of research aimed at integrating insights from modern inventory theory into the supply chain network design domain. Our approach is flexible and general enough to incorporate a variety of important side constraints into the problem.     

A novel self-adaptive control strategy of frost prevention and retardation for air source heat pumps in winter conditions

As frost accumulates on the heat exchanger surface with time, system operating performance will be dramatically degraded, and limit its use in climates susceptible to frost formation. A novel self-adaptive control strategy of frost prevention and retardation for air source heat pumps (ASHP) is introduced in this paper. The control strategy relies on a new thermodynamic model, which involves a Dimensionless Artificial Neural Network (DANN) correlation model describing frost accumulation for ASHP on the air-side of the fin-and-tube heat exchanger. The dimensionless parameters of this DANN model, including the ambient conditions, 6 commonly used refrigerants, and the geometric parameters of the heat exchanger, are considered in the model. To enhance the reliability of DANN, we develop a self-adaptive algorithm, including determining the optimal transfer algorithm and selecting the number of neurons in the hidden layer, for the DANN model. Results show a limited relative error (7.55%) between calculated values and experimental data, which help researchers and manufacturers analyze the complicated frosting process and design the new ASHPs more reasonably in different regions with different ambient conditions.     

Calculating risk neutral probabilities and optimal portfolio policies in a dynamic investment model with downside risk control

This paper presents a method for solving multiperiod investment models with downside risk control characterized by the portfolio’s worst outcome. The stochastic programming problem is decomposed into two subproblems: a nonlinear optimization model identifying the optimal terminal wealth distribution and a stochastic linear programming model replicating the identified optimal portfolio wealth. The replicating portfolio coincides with the optimal solution to the investor’s problem if the market is frictionless. The multiperiod stochastic linear programming model tests for the absence of arbitrage opportunities and its dual feasible solutions generate all risk neutral probability measures. When there are constraints such as liquidity or position requirements, the method yields approximate portfolio policies by minimizing the initial cost of the replication portfolio. A numerical example illustrates the difference between the replicating result and the optimal unconstrained portfolio.     

轴辐式同城快递网络模式研究

随着快递网点密度的稠密化,网络结构设计优劣直接关系到快递公司的运营成本和服务水平。针对快递公司的同城快递市场,在不改变现有网点规模选址的基础上改变网点的从属,结合轴辐式网络结构模式设计来提升其时效并优化成本和资源投入。以运输成本最小为目标,建立了带分支流向约束的枢纽选址模型,设计了高效的禁忌搜索算法对问题求解并验证了算法的有效性。最后提供相应的集散点选址分配解决方案,有利于整合资源形成规模效应,同时提供了同城快递分区管理依据,避免因网络结构复杂引起管理和运营混乱;从长远来看,有利于节约运营成本,增加其快递网络的柔性,降低运作管理的难度。