Modeling Analysis and Optimization of Process.and Energy Systems (original) (raw)
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Licentiate's thesis, Espoo, 2003
The main objective of this thesis is to develop a systematic methodology for cost-effective thermal integration of production plant sections and the utility system of an industrial site. A production plant section is a subprocess of a production process that transforms materials into intermediate or end products. A production process is built up from a group of these sections or sub-processes linked together in a logical way. Typically a central utility system provides the utilities that the production sections need. The methodology developed in this thesis can analyze and optimize the energy system of this kind of total site. This is done in a manner such that the production volume of the production plant sections is unaffected. Heat can be transferred between and within the production plant sections so that the total utility consumption is minimized with minimum heat transferred between sections. The minimum investments needed in the heat exchanger network are also obtained. The utility system defines the economic potential of utility savings. The methodology combines simulation, thermodynamic analysis and mathematical programming. Simulation is used to provide mass-and energy balances and to provide a solid base case. The mathematical models created in this thesis are formulated as mathematical programming problems, but they are based on insights given by thermodynamic analysis tools. Using mathematical programming models and algorithms gives the benefit that operational and investment costs can be optimized. Additional information, e.g. marginal costs, can be obtained directly from the optimization results. The mathematical model is of a sequential type, where the optimization procedure proceeds with different information at different levels. The process streams are clustered into different sections based on the assumption that investment costs related to heat exchange are smaller when heat is transferred within a section compared to investment costs related to heat exchange between sections. In this way, the problem is decomposed into smaller sub-problems so that very large optimization problems can be solved. Restrictions can be made to different flows. For example, some streams have to be heated by a hot utility but on the other hand there may be streams that either must or must not exchange heat between each other.
Most of the continuous process industries generate electricity by cogeneration using the heat energy required for the process. Electricity is also purchasedfrom external sources such as the grid and generated by internal sources such as diesel gensets. This leads to the decision problem of determining the economically optimum energy-mix during short and long term periods. Also it is important to evaluate the various technologies that can improve the energy supply system. This paper presents a mixed integer (&I) linear programming model to tackle the above decision problem and presents a case study on the application of the model. It is shown that the modelprovides the methods for determining the optimal strategies that minimize the overall cost of energyfor the process industry.
Energy Efficiency, 2019
The emphasis of this paper is to show the existence of some non-energy benefits that can be taken into account in an energy efficiency investment aimed to reduce energy consumption and increase production and product quality in an oil and gas processing center (OGPC) in México. The function of OGPC is to separate crude oil, gas, and saltwater coming from marine and terrestrial oil fields. Traditionally, application of process energy integration techniques has been aimed to reduce energy consumption associated with heating and cooling services. In this paper, the process energy integration (standard pinch analysis) of an OGPC shows the possibility of reducing natural gas and electricity consumption by 75 % and 98 %, respectively. However, the novel aspect of this work is the identifications and use of some waste heat streams available in processes to reduce energy consumption, and more importantly couple them with some non-energy-related benefits to produce massive economical savings. For example, by allowing an improvement in the quality of heavy crude oil for exportation (reduction in salt content), an increased sale price of 0.6 USD/barrel is achieved, rising profit to 156.88 MMUSD/year. Additional economic benefits came from the restauration of the production of 3,711 barrel of naphtha per day (33.86 MMUSD/ year), by solving security issues related to the use of direct-fire heaters in the condensate stabilization plant. Keywords Non-energy benefits. Energy efficiency. Heat recovery. Oil and gas processing Highlights • Non-energy benefits have been analyzed to improve the financial attractiveness of energy efficiency investments • Energy analysis can be aimed not only to reduce energy consumption but also to increase product quality and production • Waste heat recovery potentials are exploited in the context of full thermal integration of an oil and gas processing center.
A generic method for energy-efficient and energy-cost-effective production at the unit process level
Generally, industry includes various sectors like manufacturing, energy, materials & mining, and transportation. Industry consumes about one half of the world's total delivered energy, and manufacturing is one of the energy-intensive industrial sectors. With the rising energy price, the energy cost is becoming a controllable expenditure in manufacturing. In this paper, a generic method has been proposed to minimize the energy cost and improve the energy efficiency of manufacturing unit processes. Finite state machines have been used to build the transitional state-based energy model of a single machine. A mixed-integer linear programming mathematical model has been formulated for energy-cost-aware job order scheduling on a single machine. A generic algorithm has been implemented to search for an energy-cost-effective schedule at volatile energy prices with the constraint of due dates. As a result, plant managers can have an energy-cost-effective job order schedule which is associated with machine energy states along time, and can also get time-indexed energy simulation of the schedule. In comparison to most of the static scheduling approaches, stochasticity has been further handled through a cyclic interaction between the scheduler and the energy model, which facilitates to investigate how stochas-ticity on a shop floor affects the performance of energy-cost-aware scheduling. Empirical data have been used in the case study, including the power measured from a grinding machine, and the real-time pricing and time-of-use pricing tariffs. The proposed method has been demonstrated to be both energy-efficient and energy-cost-efficient even at the presence of stochasticity. As a joint effort of energy efficiency and demand response within demand side management, this method shows its effectiveness for contributing to the reduction of greenhouse gas emissions during peak periods, and for leading to energy-efficient, demand-responsive, and cost-effective manufacturing processes.
Methodology for optimization of operation to reduce site-scale energy use in production plants
Applied Thermal Engineering, 1997
methodology was developed to improve energy usage in the process industries by optimizing plant operation. The first step was to build fine tuned site-scale models of energy intensive production plants and of combined heat and power utility distribution networks (heat, power, cooling), and later to optimize them using various numerical techniques. The methodology not only comprises these models, but also the selected optimization methods, their implementation in algorithms suited for the problems to be solved and the evaluation of the results in terms of their applicability in real industrial problems. The detailed models were tuned on the basis of actual plant data. They were used to calculate accurate energy balances, and thus to identify sources of energy waste. Finally they allowed optimization of energy usage by adjusting operating conditions at a medium time scale (a few hours to a couple of days). Energy savings in the range 5%10% could be obtained for energy intensive processes, where even a low percentage represents a considerable amount.
Simultaneous process optimization and heat integration based on rigorous process simulations
Computers & Chemical Engineering, 2015
This paper introduces a simultaneous process optimization and heat integration approach, which can be used directly with the rigorous models in process simulators. In this approach, the overall process is optimized utilizing external derivative-free optimizers, which interact directly with the process simulation. The heat integration subproblem is formulated as an LP model and solved simultaneously during optimization of the flowsheet to update the minimum utility and heat exchanger area targets. A piecewise linear approximation for the composite curve is applied to obtain more accurate heat integration results. This paper describes the application of this simultaneous approach for three cases: a recycle process, a separation process and a power plant with carbon capture. Case study results indicate that this simultaneous approach is relatively easy to implement and achieves higher profit and lower operating cost and, in the case of the power plant example, higher net efficiency than the sequential approach.
Improving the efficiency of industrial processes for reducing the energy bill
Zenodo (CERN European Organization for Nuclear Research), 2022
The concept of energy analysis is defined and applied to industrial processes. The study discusses the significance of choosing the definition of efficiency, system limitations and problem definition. The purpose of this report is to show the simplicity and value of using the concept of energy analysis in the analysis of industrial processes and to develop conventions and standards in the field of process efficiency. In the situation of the Republic of Moldova, the implementation of projects to improve energy efficiency and the use of renewable energy sources contributes to reducing the degree of the energy dependence of the country and also represents a national contribution in the fight against climate change.
Energy efficiency assessment: Process modelling and waste heat recovery analysis
Energy Conversion and Management, 2019
Energy efficiency in industry is not as elevated as it should be. The aim of this paper is to present a process evaluation based on modelling as well as a waste heat recovery evaluation for a continuous heat treatment process of an Aluminium Die-Casting plant. The process is represented by production and energy dynamic (timedependent) models combining thermal phenomena with production and economic considerations. These models allow the energy consumption, resource utilization and the production schema to be evaluated. Simulated theoretical phenomena were compared and validated with real data measurements. Once validated, the model of the heat treatment process was applied to search the best work configuration and to identify, quantify and evaluate the impact of a waste heat recovery system. Based on simulation results, their viability (energy savings or productivity increase) was quantified. The assessment shows a potential to reduce the natural gas consumption in the aging heat treatment process up to 55%, with approximately a 3-years payback period and savings of 300 MWh/year. The new working way of the process is assessed. The burners of the aging treatment process present energy reductions from 50% to 80% depending on the burner position. The new waste heat recovery system provides up to 63% of the new energy required by the aging furnace.
Process analysis of an industrial waste-to-energy plant: theory and experiments
Process Safety and Environmental Protection, 2015
Thermal conversion is fundamental in an integrated waste management system due to the capability of reducing mass and volume of waste and recovering energy content from unrecyclable materials. Indeed, power generation from industrial solid wastes (ISW) is a topic of great interest for its appeal in the field of renewable energy production as well as for an increasing public concern related to its emissions. This paper is based on the process engineering and optimization analysis, commissioned to the University Campus-Biomedico of Rome by the MIDA Tecnologie Ambientali S.r.l. enterprise, ended up in the construction of an ISW thermo-conversion plant in Crotone (Southern Italy), where it is nowadays operating. The scientific approach to the process analysis is founded on a novel cascade numerical simulation of each plant section and it has been used initially in the process design step and after to simulate the performances of the industrial plant. In this paper, the plant process scheme is described together with the values of main operating parameters monitored during the experimental test runs. The thermodynamic and kinetic basics of the mathematical model for the simulation of the energy recovery and flue gas treatment sections are presented. Moreover, the simulation results, together with the implemented parameters, are given and compared to the experimental data for 10 specific plant test runs. It was found that the model is capable to predict the process performances in the energy production as well as in the gas treatment sections with high accuracy by knowing a set of measurable input variables. In the paper fundamental plant variables have been considered such as steam temperature, steam flow rate, power generated as well as temperature, flow rate and composition of the resulting flue gas; therefore, the mathematical model can be simply implemented as a reliable and efficient tool for management optimization of this kind of plants.
Modeling On-Site Combined Heat and Power Systems Coupled to Main Process Operation
Processes
Many production processes work with on-site Combined Heat and Power (CHP) systems to reduce their operational cost and improve their incomes by selling electricity to the external grid. Optimal management of these plants is key in order to take full advantage of the possibilities offered by the different electricity purchase or selling options. Traditionally, this problem is not considered for small cogeneration systems whose electricity generation cannot be decided independently from the main process production rate. In this work, a non-linear gray-box model is proposed in order to deal with this dynamic optimization problem in a simulated sugar factory. The validation shows that with only 52 equations, the whole system behavior is represented correctly and, due to its structure and small size, it can be adapted to any other production process working along a CHP with the same plant configuration.