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This work presents a mathematical modeling framework for the simulation and optimization of pressure/vacuum swing adsorption (P/VSA) processes for post-combustion CO 2 capture. A single-stage P/VSA process for CO 2 capture from dry flue gas is considered using new zeolite 13X-based adsorbents resulting from perturbation on the 13X zeolite isotherm. A two-bed six-step P/VSA cycle configuration with light product pressurization is employed in systematic simulation and optimization studies. First a zeolite 13X, the current benchmark commercial adsorbent for CO 2 capture, is considered. Accordingly, the model is used to study and evaluate new zeolite 13X-based adsorbents for more efficient CO 2 capture. The results from systematic comparative simulation studies demonstrate that a modified zeolite 13X-based adsorbent appears to have better process performance compared with the original zeolite 13X. Furthermore, process optimization studies employing the above potential adsorbents are performed to minimize energy consumption for specified minimum requirements in CO 2 purity and recovery. The optimization results indicate that the minimum target of 95% in CO 2 purity and 90% in CO 2 recovery is easily met for the P/VSA process under consideration for both potential adsorbents under different operating conditions resulting in different energy requirements and CO 2 productivity.

This work presents a generic optimization framework to address the problem of the optimal design and operational scheduling of energy microgrids. The problem to be solved is formulated as a mixed-integer linear programming (MILP) model whose objective function concerns the total cost minimization of the energy microgrid. The energy generating units to be installed consist of technologies using fuel (natural gas) as a raw material (microturbines, fuel cells etc.), and renewable energy sources (wind and solar). The microgrid is divided into a certain number of zones, each of which is characterized by a given amount of electricity demand to be satisfied, while the system can exchange electrical energy with the main power grid by acquiring from and selling energy to the grid. The efficiency and applicability of the proposed model is illustrated using three case studies. The maximum allowable level of CO2 emissions, the price of electricity purchased from the main grid, and the price of electricity sold to the main grid constitute the parameters whose influences on the economic variables of the microgrid, on the quantities and the capacities of the installed technologies, as well as on the energy balance of the microgrid are investigated. The proposed model provides a systematic and analytical methodological framework for a detailed planning and scheduling of energy microgrids, highlighting potential risks and appropriate price signals on critical energy projects undertaken by investors and/or designed by policy makers at a national and/or regional level under realistic operating conditions.

The aim of this work is to demonstrate the response of advanced model-based predictive control (MPC) strategies for Polymer Electrolyte Membrane Fuel cell (PEMFC) systems. PEMFC are considered as an interesting alternative to conventional power generation and can be used in a wide range of stationary and mobile applications. An integrated and modular computer-aided Energy Management Framework (EMF) is developed and deployed online to an industrial automation system for monitoring and operation of a PEMFC testing unit at CERTH/CPERI. The operation objectives are to deliver the demanded power while operating at a safe region, avoiding starvation, and concurrently minimize the fuel consumption at stable temperature conditions. A dynamic model is utilized and different MPC strategies are online deployed (Nonlinear MPC, multiparametric MPC and explicit Nonlinear MPC). The response of the MPC strategies is assessed through a set of comparative experimental studies, illustrating that the control objectives are achieved and the fuel cell system operates economically and at a stable environment regardless of the varying operating conditions.

Flexibility in supply chain networks dealing with uncertainty, has become a research challenge over the past years. This work proposes a flexible supply chain network design (SCND) model that uses generalized production/warehousing nodes instead of individual production plants and warehouses while conquers with demand uncertainty using a scenario-based approach. It also deals with inventory management and decisions on strategical and tactical level (facility location, production rate, warehouse capacity, demand allocation between generalized nodes, inventory levels, product flows, suppliers’ product availability and links between all facilities). The proposed Mixed-Integer Linear Programming (MILP) model allows intra-layer flows between generalized nodes and aims at minimizing total network cost. A case study is formed to test the applicability of the model for a medium sized European company. A comparison was made between a classic supply chain network and generalized network that deals with uncertainty. Results have revealed cost benefits for this model, making it not only applicable, but also cost effective for the company that will apply it. This decision support system, can help managers in taking strategic decisions such as facility location, with a higher level of accuracy.

A distributed energy system refers to an energy system where energy production is close to end use, typically relying on small-scale energy distributed technologies. It is a multi-input and multi-output energy system with substantial energy, economic and environmental benefits. However, distributed energy systems such as micro-grids in residential applications may not be able to produce the potential benefits due to lack of appropriate system configurations and suitable operation strategies. The optimal design, scheduling and control of such a complex system are of great importance towards their successful practical realization in real application studies. This paper presents a short review and an energy systems engineering approach to the modeling and optimization of micro-grids for residential applications, offering a clear vision of the latest research advances in this field. Challenges and prospects of the modeling and optimization of such distributed energy systems are also highlighted in this work.

This work presents the detailed mathematical modelling and advanced optimization of a number of zeolite membrane process structures for gas separations. First, a general model for the separation of binary gas mixtures is developed based on the generalized Maxwell–Stefan (GMS) approach. Accordingly, the model is used in the simulation of various process structures including co-current and counter-current operation, recycle modules and membrane cascades. The model is validated against available experimental data of methane-ethane and methane–propane mixtures on silicalite membranes. A non-linear programming approach is then employed to determine optimal design options for different process performance objectives. Various trade-offs between different optimization objectives are systematically revealed. The impact of the detailed GMS model on the optimization results is investigated through a comparison with corresponding results obtained using the single-file diffusion model, which ignores diffusional adsorbate–adsorbate interactions.

This work presents a new mathematical programming formulation for the problem of scheduling cutting operations on multiple parallel slitting machines. A Mixed-Integer Linear Programming (MILP) model is proposed, solved to optimality using standard techniques. A continuous time representation is used to avoid unnecessary time intervals and to limit the number of variables in the formulation. One important feature of the model is the explicit treatment of change-over times as an important part of a multiple objective cost function, which may be adapted to minimizing makespan, or total flow time, or any weighted combination of the two. An industrial case study from the paper-converting industry is presented to illustrate the applicability and efficiency of the proposed model.

This paper addresses the problem of cyclic cleaning and energy scheduling in special classes of heat exchanger networks (HENs). A salient characteristic of this problem is that the performance of each heat exchanger decreases with time and can then be restored to its initial state by performing cleaning operations. Due to the cyclic nature of the schedule, some operations may span successive cycles (wrap-around) which is taken into account in the mathematical model. A tight mixed integer linear programming (MILP) model is presented which is solved to global optimality. A detailed objective function is used to account for cleaning cost and energy requirements. The formulations can model serial and parallel HENs, as well as network arrangements arising from the combination of these basic cases. The optimization algorithm determines simultaneously: (i) the number of cleaning operations required along with their corresponding timings and (ii) the optimal utility utilization profile over time. A complex heat exchanger network example is presented to illustrate the applicability of the proposed model.