• Energy Research

Abstract An exergy analysis, which only considers the unavoidable exergy destruction, is conducted for single, double, triple and half effect Water–Lithium bromide absorption cycles. Thus, the obtained performances represent the maximum achievable performance under the given operation conditions. The coefficient of performance (COP), the exergetic efficiencies and the exergy destruction rates are determined and the effect of the heat source temperature is evaluated. As expected, the COP increases significantly from double lift to triple effect cycles. The exergetic efficiency varies less among the different configurations. In all cycles the effect of the heat source temperature on the exergy destruction rates is similar for the same type of components, while the quantitative contributions depend on cycle type and flow configuration. Largest exergy destruction occurs in the absorbers and generators, especially at higher heat source temperatures.

Abstract A rigorous mathematical approach is developed for optimization of sustainable single-effect water/Lithium Bromide (LiBr) absorption cooling cycles. The multi-objective formulation accounts for minimization of the chiller area as well as the environmental impact associated with the operation of the absorption cycle. The environmental impact is quantified based on the global warming potential and the Eco-indicator 99, both of which follow principles of life cycle assessment. The design task is formulated as a bi-criterion non-linear programming problem, the solution of which is defined by a set of Pareto points that represent the optimal compromise between the total area of the chiller and global warming potential. These Pareto sets are obtained via the epsilon constraint method. A set of design alternatives are provided for the absorption cycles rather than a single design; the best design can be chosen from this set based on the major constraints and benefits in a given application. The proposed approach is illustrated design of a typical absorption cooling cycle.

In this paper, a systematic method based on mathematical programming is proposed for the design of environmentally conscious absorption cooling systems. The approach presented relies on the development of a multi-objective formulation that simultaneously accounts for the minimization of cost and environmental impact at the design stage. The latter criterion is measured by the Eco-indicator 99 methodology, which follows the principles of life cycle assessment (LCA). The design task is formulated as a bi-criteria nonlinear programming (NLP) problem, the solution of which is defined by a set of Pareto points that represent the optimal trade-off between the economic and environmental concerns considered in the analysis. These Pareto solutions can be obtained via standard techniques for multi-objective optimization. The main advantage of this approach is that it offers a set of alternative options for system design rather than a single solution. From these alternatives, the decision-maker can choose the best one according to his/her preferences and the applicable legislation. The capabilities of the proposed method are illustrated in a case study problem that addresses the design of a typical absorption cooling system.

In recent years, there has been a growing increase of the cooling demand in many parts of the world, which has led to major energy problems. In this context, solar assisted absorption cooling systems have emerged as a promising alternative to conventional vapor compression air conditioning systems, given the fact that in many cases the cooling demand coincide with the availability of solar radiation. In this work, we present a decision-support tool based on mathematical programming for the design of solar assisted absorption cooling systems. The design task is formulated as a bi-criteria mixed-integer nonlinear programming (MINLP) optimization problem that accounts for the minimization of the total cost of the cooling system and the associated environmental impact measured over its entire life cycle. The capabilities of the proposed method are illustrated in a case study that addresses the design of a solar assisted ammonia-water absorption cycle considering weather data of Barcelona (Spain).

Abstract This paper proposes a multiobjective, mixed-integer nonlinear programming (MINLP) model for the superstructure optimization of hydrocarbon biorefineries via gasification pathway under economic and environmental criteria. The proposed hydrocarbon biorefinery superstructure includes a number of major processing stages, such as drying of the cellulosic biomass feedstocks, air separation unit, gasification, syngas conditioning, Fischer–Tropsch synthesis, hydroprocessing, power generation, and the diesel and gasoline production. The superstructure considers alternatives of technologies and equipment, such as gasification technologies, cooling options, hydrogen production sources, and Fischer–Tropsch synthesis catalysts. The economic objective is measured by the net present value ( NPV ), and the environmental concern is measured using global warming potential ( GWP ) that follows the life cycle assessment procedures, which evaluates the gate-to-gate environmental impacts of hydrocarbon biofuels. The multiobjective MINLP model simultaneously determines the technology selection, operation conditions, flow rate of each stream, energy consumption of each unit, economic performance, environmental impacts, and equipment sizes. The multiobjective MINLP problem is solved with the ɛ -constraint method. The resulting Pareto-optimal curve reveals the trade-off between the economic and environmental performances. The optimal solution reveals that the high-temperature gasification, direct cooling, internal hydrogen production and cobalt catalysis have the best environmental and economic performances. At the breakeven point, where the optimal NPV is 0, the unit production cost of hydrocarbon biorefinery is $4.43 per gasoline-equivalent gallon (GEG) with unit GWP of 20.92 kg CO 2 eqv./GEG. In the case of maximum NPV of $810 MM, the corresponding unit production cost is $3.17/GEG.

Abstract This work addresses the use of absorption cycles combined with solar energy for reducing the green house gas (GHG) emissions in the cooling sector. The problem of satisfying a given cooling demand at minimum cost and environmental impact is formulated as a bi-criterion non-linear optimization problem that seeks to minimize the total cost of the cooling application and its contribution to global warming. The latter metric, which is assessed following the principles of life cycle assessment (LCA), accounts for the impact caused during the construction and operation of the system. The concept of Pareto optimality is employed to discuss different alternatives for reducing the contribution to global warming that differ in their economic and environmental performance. We also analyze the effect of taxes on CO 2 on the economic and environmental performance of the system. The capabilities of the proposed approach are illustrated through a case study that addresses the design of a solar assisted ammonia-water single effect absorption cooling system with 100 kW of cooling capacity considering Barcelona weather conditions. We show that reducing the contribution to global warming considering the current energy prices and taxes on carbon dioxide emissions is technically viable but economically not appealing. We also discuss the conditions under which reducing the CO 2 emissions could become economically attractive.

Abstract This paper addresses the optimal design and operation of hydrocarbon biorefinery via fast pyrolysis, hydrotreating and hydrocracking of hybrid poplar feedstock under economic and environmental criteria. The hydrocarbon biorefinery encompasses fast pyrolysis for crude bio-oil production, upgrading of the bio-oil through hydrotreating, separation and hydrocracking of long chained hydrocarbons into gasoline and diesel range products, and steam reforming for hydrogen production. We propose a bi-criteria nonlinear programming (NLP) model that seeks to maximize the economic performance measured by the net present value (NPV) and to minimize the environmental impacts. The environmental objective is measured with the global warming potential (GWP) metric according to the life cycle assessment procedures, which covers gate-to-gate environmental impacts of the hydrocarbon biorefinery. The multiobjective NLP model simultaneously determines the production capacity, size of each process units, operational conditions, the flow rates of species and streams at each stage of the process, hydrocarbon biofuel yields, and consumption rate of feedstock, steam, electricity, and natural gas. The bi-criteria NLP model is solved with the ɛ -constraint method, and the resulting Pareto-optimal curve reveals the trade-off between the economic and environmental dimensions of the sustainable hydrocarbon biorefinery. The optimization results reveal that the unit production cost of the hydrocarbon biofuels is $2.31 per gallon of gasoline equivalent (GGE) for the maximum NPV solution and $3.67/GGE for the minimum GWP design. The corresponding greenhouse emission is 8.07 kgCO 2-eq /GGE.

Many of the strategies devised so far to address the optimization of energy systems are deterministic approaches that rely on estimated data. However, in real world applications there are many sources of uncertainty that introduce variability into the decision-making problem. Within this general context, we propose a novel approach to address the design of absorption cooling systems under uncertainty in the energy cost. As opposed to other approaches that optimize the expected performance of the system as a single objective, in our method the design task is formulated as a stochastic bi-criteria non-linear optimization problem that simultaneously accounts for the minimization of the expected total cost and the financial risk associated with the investment. The latter criterion is measured by the downside risk, which avoids the need to define binary variables thus improving the computational performance of the model. The capabilities of the proposed modeling framework and solution strategy are illustrated in a case study problem that addresses the design of a typical absorption cooling system. Numerical results demonstrate that the method presented allows to manage the risk level effectively by varying the area of the heat exchangers of the absorption cycle. Specifically, our strategy allows identifying the optimal values of the operating and design variables of the cycle that make it less sensitive to fluctuations in the energy price, thus improving its robustness in the face of uncertainty.