Simulation-optimization of solar-thermal refrigeration systems for office use in subtropical Hong Kong

The solar-thermal refrigeration systems, covering the solar absorption refrigeration system (SAbRS) and the solar adsorption refrigeration system (SAdRS), were designed for typical office in the subtropical Hong Kong. The approach of simulation-optimization was adopted in order to determine the optimal design parameters for SAbRS and SAdRS against the conventional design practice. For simulation, dynamic model of each system was refined on the TRNSYS platform. For optimization, the objective was to minimize the annual primary energy consumption of SAbRS or SAdRS in response to the changing loading and climatic conditions throughout a year. This is a constrained optimization problem since the upper limit of comfort temperature was stipulated, such that the minimization of system energy would not sacrifice the indoor thermal comfort. Due to the complex, multidimensional and constrained nature of the dynamic simulation models, the differential evolution (DE), which has been proven effective in evolutionary computation (EC), was used for optimization purpose. Through the simulation-optimization run, the optimized designs of SAbRS and SAdRS were determined, and their corresponding primary energy consumptions could be 12.2% and 7.1% less than those based on the general design practice. The results provide useful guidelines for the equipment design of SAbRS and SAdRS.     

A knowledge-based simulation-optimization framework and system for sustainable process operations

Design and operation of chemical plants involves a combination of synthesis, analysis and evaluation of alternatives. Such activities have traditionally been driven by economic factors first, followed by engineering, safety and environmental considerations. Recently, chemical companies have embraced the concept of sustainable development, entailing renewable feed materials and energy, non-toxic and biodegradable products, and waste minimization or even elimination at source. In this paper, we introduce a knowledge-based simulation-optimization framework for generating sustainable alternatives to chemical processes. The framework has been developed by combining different process systems engineering methodologies - the knowledge-based approach for identifying the root cause of waste generation, the hierarchical design method for generating alternative designs, sustainability metrics, and multi-objective optimization - into one coherent simulation-optimization framework. This is implemented as a decision-support system using Gensym's G2 and the HYSYS process simulator. We illustrate the framework and system using the HDA and biodiesel production case studies.     

A multi-objective approach for determining optimal air compressor location in a manufacturing facility

Determining the optimal location of an air compressor in a manufacturing facility is a challenging problem that can offer significant energy savings. A novel simulation-optimization model is proposed to increase energy efficiency in a facility by determining optimal air compressor location. The optimization strategy is based on an objective function that minimizes the total energy consumption of the air compressor – hence, the energy cost for the facility – while considering the user's preference for the air compressor location. The proposed mathematical model first integrates the facility's characteristics based on user inputs, divides the facility into zones, and generates a rectilinear zone-to-zone distance matrix within the facility. The user location preference is incorporated into the proposed model via a five level user-preference index, assigned using preferential locations as suggested by twenty-two experienced facility managers. A sensitivity analysis is conducted to determine the relationship between the selected user preference level and the resulting energy consumption at each location in the facility. A simulation-driven analysis is performed using a real-life facility layout and typical compressed air equipment with corresponding nameplate data. In order to investigate and demonstrate the effectiveness of the proposed approach, the derived optimal zones are compared with five zones, including the most energy efficient zone, least energy efficient zone, and three other zones selected at random. The results of our study reveal that the proposed method achieves significant energy reductions while maintaining the user's desired air compressor location.     

f-MOPSO: An alternative multi-objective PSO algorithm for conjunctive water use management

In recent years, evolutionary techniques have been widely used to search for the global optimum of combinatorial non-linear non-convex problems. In this paper, we present a new algorithm, named fuzzy Multi-Objective Particle Swarm Optimization (f-MOPSO) to improve conjunctive surface water and groundwater management. The f-MOPSO algorithm is simple in concept, easy to implement, and computationally efficient. It is based on the role of weighting method to define partial performance of each point (solution) in the objective space. The proposed algorithm employs a fuzzy inference system to consider all the partial performances for each point when optimizing the objective function values. The f-MOPSO algorithm was compared with two other well-known MOPSOs through a case study of conjunctive use of surface and groundwater in Najafabad Plain in Iran considering two management models, including a typical 12-month operation period and a 10-year planning horizon. Overall, the f-MOPSO outperformed the other MOPSO algorithms with reference to performance criteria and Pareto-front analysis while nearly fully satisfying water demands with least monthly and cumulative groundwater level (GWL) variation. The proposed algorithm is capable of finding the unique optimal solution on the Pareto-front to facilitate decisions to address large-scale optimization problems.     

Simulation-Optimization Modelling for Sustainable Groundwater Management in a Coastal Basin of Orissa, India

The Balasore coastal groundwater basin of Orissa in eastern India is under a serious threat of overdraft and seawater intrusion. Two optimization models were developed in this study for the efficient utilization of water resources in Balasore basin during non-monsoon periods: (a) a non-linear hydraulic management model for optimal pumpage, and (b) a linear optimization model for optimal cropping pattern in integration with a calibrated and validated groundwater flow simulation model. Based on the simulation-optimization modeling results, optimal pumping schedules, cropping patterns, and corresponding groundwater conditions are presented for three scenarios viz., wet, normal and dry years. It was found that optimal pumping schedules and corresponding cropping patterns differed significantly under the three scenarios, and the groundwater levels improved significantly under the optimal hydraulic conditions compared to the existing condition. In dry years, the groundwater levels under the present pumping pattern and the optimal pumpage indicated that the non-monsoon pumpage should not exceed the optimal pumpage in the absence of remedial measures in the basin. It is concluded that in order to ensure sustainable groundwater utilization in the basin, the optimal cropping pattern and pumping schedule should be adopted by the farmers.     

Maintenance policies with two-dimensional warranty

A new maintenance policy which minimizes the total expected servicing cost for an item with two-dimensional warranty is proposed. An iterative procedure to estimate the item's failure rate function from historical observations and an optimization algorithm based on Monte Carlo simulation are applied to obtain the best maintenance policy. Numerical examples and sensitivity analysis are presented.     

Simulation-based modeling and optimization for refinery hydrogen network integration with light hydrocarbon recovery

Purge gases from hydrocrackers and hydrotreaters and refinery off-gases are important hydrogen sources. Some of these hydrogen sources are also rich in light hydrocarbons that are valuable energy resources and chemical materials. In this work, a systematic method is proposed to integrate hydrogen networks considering light hydrocarbon recovery. This work first develops a hydrogen network superstructure with light hydrocarbon recovery. Aspen HYSYS is employed for rigorous process and thermodynamic modeling of the light hydrocarbon recovery process, and a simulation-optimization model is then developed. To solve the simulation-optimization model efficiently, the genetic algorithm is used as the global solver to determine the feed to light hydrocarbon recovery unit, and the linprog and fmincon solvers are combined to determine the optimal hydrogen network design. The application and effectiveness of the proposed method is validated through a case study. The results show that fresh hydrogen consumption decreases by 13% and the total annualized cost reduces to 72% because of light hydrocarbon recovery. This method could provide useful guides for the management of hydrogen and light hydrocarbons in refineries.     

Scheduling optimization of a stochastic flexible job-shop system with time-varying machine failure rate

Due to environmental circumstances encountered in manufacturing processes, operating machines need to be maintained preventively, so as to ensure satisfactory operating condition. This paper investigates a scheduling problem in a flexible job-shop system with maintenance considerations where each operation can be processed by a machine out of a set of capable machines, and so, jobs may have alternative routes. Machine failure rates are assumed to be time-varying. This is a real assumption comes from a fact in realistic environments, where failure rate of a machine is variable when environmental situations like shop temperature, shop light, shop humidity or even worker skill change significantly. Moreover, in order to more close the addressed problem into the situations encountered in real world, the processing times and due dates are considered to be stochastic parameters. A mixed integer linear programming (MILP) model is constructed for addressed problem with the objective of number of tardy jobs and a minimum total availability constraint. Then a simulation-optimization framework based on a simulated annealing (SA) optimizer and Monte Carlo (MC) simulator is presented to solve the problem.     

Groundwater cleanup: The optimization perspective (a literature review)

Contamination of groundwater resources is becoming a serious environmental problem. Huge investments are usually needed to contain and to restore contaminated groundwater, thus reducing costs is challenging. Simulation-optimization techniques can obviously play an important role here. The application of these techniques to reducing remediation costs has become an area of active research, and much progress has been made towards developing mathematical models for groundwater management and remediation. These models are built with a view to aiding the proper identification of the most cost-effective measures to be taken, while satisfying a set of stated physical, technological, legal and other constraints. The literature shows that during the last fifteen years a great deal of work has been done on this subject. This paper presents a synthesis of the optimization models reported in the literature on this topic, and discusses both their mathematical characteristics and their suitability.