MULTI-OBJECTIVE DIFFERENTIAL EVOLUTION: MODIFICATIONS AND APPLICATIONS TO CHEMICAL PROCESSES

Ph.DDOCTOR OF PHILOSOPH

Robust multi-objective optimization of solid oxide fuel cell–gas turbine hybrid cycle and uncertainty analysis

Chemical process optimization problems often have multiple and conflicting objectives, such as capital cost, operating cost, production cost, profit, energy consumptions and environmental impacts. In such cases, Multi-Objective Optimization (MOO) is suitable in finding many Pareto optimal solutions, to understand the quantitative trade-offs among the objectives, and also to obtain the optimal values of decision variables. Gaseous fuel can be converted into heat, power and electricity, using combustion engine, gas turbine (GT) or Solid Oxide Fuel Cell (SOFC). Of these, SOFC with GT has shown higher thermodynamic performance. This hybrid conversion system leads to a better utilization of natural resource, reduced environmental impacts, and more profit. This study optimizes performance of SOFC-GT system for maximization of annual profit and minimization of annualized capital cost, simultaneously. For optimal SOFC-GT designs, the composite curves for maximum amount of possible heat recovery indicate good performance of the hybrid system. Further, first law energy and exergy efficiencies of optimal SOFC-GT designs are significantly better compared to traditional conversion systems. In order to obtain flexible design in the presence of uncertain parameters, robust MOO of SOFC-GT system was also performed. Finally, Pareto solutions obtained via normal and robust MOO approaches are considered for parametric uncertainty analysis with respect to market and operating conditions, and solution obtained via robust MOO found to be less sensitive

OBSTACLE DETECTION AND ELECTRONIC NAVIGATION SYSTEM FOR VISUALLY IMPAIRED PERSONS

In This Paper we present a real time domain obstacle detection system for the visually impaired persons to improve their mobility in daily life with the help of obstacle detection sensor installed in their walking stick .System is having a lower cost so it is easily purchasable so it can have a major significance in life of visually impaired persons. This Paper proposes a system to detect any object attached to the floor regardless to their height [1]. Obstacle on the floor in the front of user can be reliably detected in real time using the proposed system implemented by the IR sensor installed on the walk stick of the visually impaired person. Project also contains a navigation system for visually impaired persons to make the life of such persons easier up to some extent. This project is suited for the area where the possibility of blind person is high (like blind school, college)[6]. For transport facility of blind we have first decided the common bus roots of blind then we have placed RF tag to all those buses with unique code. At the second side we have placed RF reader, microcontroller and voice processor. The RF reader receive unique code, microcontroller process this code with defined code, if match found, voice processor get activated and starts speaking bus name, initial destination and final destination. The obstacle detection is also included in the project with voice. The system aims at increasing the mobility of visually impaired people by offering new sensing abilities

Multi-Objective Optimization Programs and their Application to Amine Absorption Process Design for Natural Gas Sweetening

This chapter presents three MS Excel programs, namely, EMOO (Excel based Multi-Objective Optimization), NDS (Non-Dominated Sorting) and PM (Performance Metrics) useful for Multi-Objective Optimization (MOO) studies. The EMOO program is for finding non-dominated solutions of a given MOO problem. It has both binary-coded and realcoded NSGA-II (Elitist Non-Dominated Sorting Genetic Algorithm), and two termination criteria based on chi-squared test and steady state detection. The known/true Pareto-optimal front for the application problems is not available unlike that for benchmark problems. Hence, a procedure for obtaining known/true Pareto-optimal front is described in this chapter. The NDS program is for non-dominated sorting and crowding distance calculations of the non-dominated solutions obtained from several optimization runs using same or different MOO programs. The PM program can be used to calculate the values of performance metrics between the non-dominated solutions obtained using a MOO program and the true/known Pareto optimal front. It is useful for comparing the performance of MOO programs to find the non-dominated solutions. Finally, use of EMOO, NDS and PM programs is demonstrated on MOO of amine absorption process for natural gas sweetening

Carbon Dioxide Capture From Internal Combustion Engine Exhaust Using Temperature Swing Adsorption

In order to reduce the CO2 emissions in the transportation sector, one can electrify the vehicle, switch to biofuel, or capture and store CO2 on board. In this study, integration of an on board CO2 capture and storage unit with an internal combustion engine has been proposed. The technology can be applied for various internal combustion or Stirling engines with targeted applications in the transportation sector. Truck transport for goods delivery is used as an example for on board CO2 capture and storage system design. The investigated system integrates a temperature swing adsorption system for CO2 capture with a turbo-compressor system to compress and liquefy the captured CO2 using the waste heat of the exhaust gases of the engine. Energy and exergy analyses of the proposed CO2 captured system are studied in details. The CO2 capture system for engine exhaust stream (car, truck, bus, ship, or train) can capture 90% of the emitted CO2, without any energy penalty. This system can be integrated into overall mobility system (fuel-engine-CO2-fuel), where captured CO2 can be recycled as conventional liquid or gaseous fuels produced from renewable energy sources

System for co2 capture from internal combustion engine

System (2) for CO2 capture from a combustion engine (1) comprising an exhaust gas flow circuit (6) having an inlet end fluidly connected to an exhaust of the combustion engine, a heat exchanger circuit (12), a primary exhaust gas heat exchanger (H1) for transferring heat from exhaust gas to fluid in the heat exchanger circuit, at least one compressor (10) for compressing fluid in a section of the heat exchanger circuit, the compressor driven by thermal expansion of heat exchanger circuit fluid from the primary exhaust gas heat exchanger (H1), and a CO2 temperature swing adsorption (TSA) reactor (4) fluidly connected to an outlet end of the exhaust gas flow circuit. The TSA reactor includes at least an adsorption reactor unit (D4) and a desorption reactor unit (D2), the heat exchanger circuit comprising a heating section (12b) for heating the desorption unit (D2) and a cooling section (12a) for cooling the adsorption unit (D4)

Robust multi-objective optimization of gasifier and solid oxide fuel cell plant for electricity production using wood

Biomass is an attractive renewable and stored energy that can be converted to transportation fuels, chemicals and electricity using bio-chemical and thermo-chemical conversion routes. Notably, biofuels have relatively lower greenhouse gas emissions compared to the fossil fuels. A biomass gasifier can convert lignocellulosic biomass such as wood into syngas, which can be used in Solid Oxide Fuel Cell (SOFC) to produce heat and electricity. SOFC has very good thermodynamic conversion efficiency for converting methane or hydrogen into electricity, and integration of SOFC with gasifier gives heat integration opportunities that allow one to design systems with electricity production efficiencies as high as 70%. Generally, process design and operational optimization problems have conflicting performance objectives, and Multi-Objective Optimization (MOO) methods are applied to quantify the trade-offs among the objectives and to obtain the optimal values of design and operating parameters. This study optimizes biomass gasifier and SOFC plant for annual profit and annualized capital cost, simultaneously. A Pareto front has been obtained by solving Moo problem, and then net flow method is used to identify some optimal solutions from the Pareto front for the implementation into next phase. The constructed composite curves, which notify maximum amount of possible heat recovery, and first law efficiency also indicate better performance of the integrated plant. Uncertainty of market and operating parameters has been added to the optimization problem, and robust MOO of the integrated plant has been performed, which retains less sensitive Pareto solutions during the optimization. Finally, Pareto solutions obtained via normal and robust MOO approaches are considered for uncertainty analysis, and Pareto solutions obtained via robust MOO found to be less sensitive. (C) 2017 Elsevier Ltd. All rights reserved

Designing, Retrofitting, and Revamping Water Networks in Petroleum Refineries Using Multiobjective Optimization

In the chemical industry, water integration is beneficial from the point of both economic and environmental impacts. An optimal water network reduces fresh water consumption by efficient reuse and recycling of water within the plant. In general, water networks are designed based on a single performance criterion such as fresh water consumption or total annual cost. In this study, the performance of Premium Solver in Excel is compared with BARON (Branch-And-Reduce Optimization Navigator) in GAMS (General Algebraic Modeling System) for optimizing three industrial water networks for a single objective. Recently, water networks have been studied using multiobjective optimization (MOO) for two or more objectives simultaneously. Hence, two existing refinery water networks were retrofitted and revamped using the MOO approach, for two important scenarios: the addition of two new water-using processes in the refinery, and use of contaminated water (steam condensate) along with fresh water in water networks. In the water network retrofit, only the capacities of the regeneration units can change, and there is no change in the existing network topology. Both existing water network topology and capacities of different regeneration units can change in the water network revamping. In this, MOO problems are successfully solved using the ε-constraint method along with BARON. The obtained Pareto-optimal results are presented and discussed; they give greater insight and many optimal solutions for selection