Utility System Optimization Research Articles

Life cycle assessment and multi-objective optimization for industrial utility systems

Utility systems, which are energy and CO2 intensive, provide power and heat for the industrial process. Modeling, assessing, and optimization of utility systems can help promote sustainable development. This paper proposed a life cycle assessment-based multi-objective optimization framework to address this issue. First, measures for saving energy and reducing emissions were proposed to improve the utility system. The semi-empirical models of the basic components in the improved utility system are developed using process mechanisms and historical data. Secondly, the theory of life cycle assessment (LCA) is employed to evaluate the environmental impacts comprehensively. The operating cost and environmental impact models of the system are then developed and solved by a weighted multi-objective optimization method. Finally, a case study from an industrial utility system is implemented to verify the effectiveness of the proposed method, and three scenarios with different emission reduction methods are compared. In the scenario with two emission reduction measures, it shows that the maximum reduction of environmental impacts could be 56.59%, while the operating cost increases by 36.17%. The Pareto frontiers of the three scenarios provide several choices to balance the operating cost and environmental impacts.

Style: A new optimization model for Synthesis of uTility sYstems with steam LEvel placement

Moving industrial production to a more sustainable basis requires a step change in the efficiency of site steam and cogeneration systems, coupled with a switch away from fossil fuels to renewable energy sources. This paper presents the first step in the development of an optimization framework capable of identifying a roadmap to move utility systems to a more sustainable basis. Previous approaches to the design and optimization of steam and cogeneration systems have simplified the problem to the extent that many important practical issues have been neglected, restricting the scope of the options included. The use of grossly simplified models has been prompted by the mathematical difficulties of optimizing such complex energy systems. To overcome limitations in previous work and provide a sound basis for future developments, this paper proposes a new superstructure-based optimization model for the optimization of utility systems, accounting for optimum steam level placement. The latter is important for improving systems efficiency and reducing energy consumption. The optimization problem involves the selection of more realistic operating conditions of the steam mains (superheat conditions and pressure). The model accounts for water preheating, as well as superheating and de-superheating for process steam generation and use. Hot oil circuits are also included as hot utility option to overcome potential steam temperature and/or pressure limitations at high temperatures, and with it provide more flexibility in the framework. The general problem requires making several continuous and discrete decisions, where non-linearities and non-convexities from underlying physics and binary decisions exacerbate the complex nature of the problem, yielding a nonconvex Mixed Integer Non-Linear Programming (MINLP) formulation. However, MINLP formulations can become computationally intractable to solve. Thus, to guarantee tractability and fast conversion, the STYLE model develops a novel successive Mixed Integer Linear Programming formulation. The STYLE methodology is applied to two case studies to illustrate the advantages of the synthesis method and the benefits of optimizing steam levels for the reduction of overall energy consumption at industrial sites. The proposed approach addresses major shortcomings inherent in previous research and provides a foundation for future work to explore the next generation of sustainable utility systems.

Life cycle based optimal design of utility system in offshore plants

Offshore plants have many inherent constraints and risks compared to onshore plants, and it is crucial to optimize the operation of the offshore plant by adhering to constraints. Previous studies proposed a framework of utility systems that operate under various conditions. However, few studies have addressed the optimization of utility systems during the operating life cycle. To fill in this gap, we propose a new methodology for the design and optimization of the utility systems in the offshore plants. The utility systems are designed and optimized in Aspen Utilities Planner, considering the full life cycle of the oil and gas wells. For this, we first developed steady-state models of the topside processes based on every feasible operating scenario throughout the full life cycle. Then the utility consumption data was extracted from the simulation results and analyzed. The power system was designed using Aspen Utilities Planner (AUP), and it was optimized to maximize the thermal efficiency of the utility system, satisfying power demand for all the scenarios. The optimized results illustrate the significant saving of the operating cost and reduction of CO2 emission rate. Our studies suggest a generic framework to design utility systems in offshore plants.

A data-driven approach for industrial utility systems optimization under uncertainty

Energy optimization of utility system helps to reduce the operating cost and save energy for the industrial plants. Widespread uncertainties such as device efficiency and process demand pose new challenges for this issue. A hybrid modeling framework is presented by introducing the operating data into mechanism model to adapt the changes of device efficiency and operating conditions. Mathematical models of boilers, steam turbines, and letdown valves are then developed in the framework. Based on the process historical data of a real-world plant, a Dirichlet process mixture model is used to capture the support information of uncertain parameters. Bridging data-driven robust optimization (DDRO) and utility system optimization under uncertainty, a robust mixed-integer nonlinear programming (MINLP) model is developed by utilizing the derived uncertainty set. The robust counterpart of the developed model can be reformulated as a tractable MINLP problem including conic quadratic constraints that could be solved efficiently. A real-world case study is carried out to demonstrate the effectiveness of the proposed approach in protecting against uncertainties and achieving a good trade-off between optimality and robustness of the operational decisions for industrial utility systems.

Site utility system optimization with operation adjustment under uncertainty

Utility systems must satisfy process energy and power demands under varying conditions. The system performance is decided by the system configuration and individual equipment operating load for boilers, gas turbines, steam turbines, condensers, and let down valves. Steam mains conditions in terms of steam pressures and steam superheating also play important roles on steam distribution in the system and power generation by steam expansion in steam turbines, and should be included in the system optimization. Uncertainties such as process steam power demand changes and electricity price fluctuations should be included in the system optimization to eliminate as much as possible the production loss caused by steam power deficits due to uncertainties. In this paper, uncertain factors are classified into time-based and probability-based uncertain factors, and operation scheduling containing multi-period equipment load sharing, redundant equipment start up, and electricity import to compensate for power deficits, have been presented to deal with the happens of uncertainties, and are formulated as a multi-period item and a recourse item in the optimization model. There are two case studies in this paper. One case illustrates the system design to determine system configuration, equipment selection, and system operation scheduling at the design stage to deal with uncertainties. The other case provides operational optimization scenarios for an existing system, especially when the steam superheating varies. The proposed method can provide practical guidance to system energy efficiency improvement.

Mathematical modeling, validation, and operation optimization of an industrial complex steam turbine network-methodology and application

Complex turbine (i.e., steam turbine with multiple controlled and uncontrolled extractions) is one of the most important components in industrial utility system. The function of complex turbine is to generate power electricity, provide steam for the processes, and heat boiler feedwater using uncontrolled extraction steam. Although many studies have focused on the modeling and optimization of utility systems of different scales, few works have been reported on the modeling and operation optimization for utility system containing multiple complex steam turbines. In this paper, a superstructure of utility network consists of multiple interconnected various types of complex turbines (ICSTUN) is constructed. The superstructure demonstrates not only an external utility network that supplies utility energy for the processes but also an internal utility network that supplies boiler feedwater. A systematic modeling approach is developed for robust simulation, validation and operation optimization of ICSTUN. A mixed-integer nonlinear programming (MINLP) model based on an improved modeling principle of complex turbine is formulated for the operation optimization of ICSTUN. The MINLP model is able to simultaneously optimize the external and internal utility networks. A solving strategy incorporating model decomposition, model relaxation and case initialization is proposed to find a feasible solution. A large utility network in a petrochemical complex is studied to test the accuracy of the complex turbine model and the effectiveness of the proposed operation optimization methodology. The validation results of the complex turbine model show that the mean relative error between field operation data and simulation data is less than 1.18% and is acceptable in engineering application. The optimization of a single utility plant yields a maximum coal reduction rate of 4.59%, and the optimization of the total utility system consisting of two utility plants yields a maximum coal reduction rate of 6.01%. The optimization contribution is discussed in detail, and several conclusions and suggestions are presented.

New procedure for design and exergoeconomic optimization of site utility system considering reliability

An examination is reported of the effects of equipment reliability on the exergoeconomic analysis and optimization of a site utility system. A new procedure is proposed that incorporates a hybrid method that has several advantages over the commonly used approach. The steam network design has a significant impact on the utility system, and the availability of steam mains is taken into account. An analysis of the availability of system components identifies components that can affect system operation. For this purpose, the state-space and the continuous Markov methods are incorporated in thermoeconomic analysis to improve the economic costs. Additionally, the design and optimization of utility systems potentially involve a large number of alternatives that, if properly designated, can yield significant savings in total costs. In this regard, the proposed new procedure addresses the optimal design of site utility systems. Tools applied to assist process integration include total site analysis, site utility grand composite curves (SUGCCs), the cogeneration targeting method and exergoeconomic analysis. Furthermore, multi-objective exergoeconomic optimization is performed using a genetic algorithm based on hybrid techniques in order to determine optimum solutions. The applicability of the proposed procedure is tested using STAR® software through a case study for grassroot problem.

A novel graphical approach to target CO2 emissions for energy resource planning and utility system optimization

Many optimization techniques, both numerical and graphical, have recently been introduced for carbon dioxide (CO2) emissions targeting. These targeting approaches have been applied to a wide range of applications including energy allocation and utility systems optimization. However, the trade-off between the operating cost and the associated CO2 emissions cannot be easily assessed. This paper presents a new simple graphical approach to target CO2 emissions associated with energy resources and utility systems. The principles of marginal energy cost and marginal CO2 emissions are employed to construct two composite curves to be used as targeting tools. The CO2 emissions composite curve (CO2CC) and cost composite curve (CCC) are used to determine the minimum cost associated with increasing energy demand whilst simultaneously meeting a given CO2 emissions target. Multiple trade-off solutions so called Pareto optimal solutions can be generated using a range of emissions targets. The new graphical approach can be used to target CO2 emissions related to utility systems and energy resources networks. Two case studies are used to demonstrate this targeting method.

Reliability issues in the design and optimization of process utility systems

The reliability of process utility systems is a prime consideration for both their design and operation. This paper combines the optimization of utility system performance with reliability theory based on Markov analysis. Operation mode is first to be considered, e.g. hot standby, cold standby, load-sharing, etc. Downtime during start up has been considered for the first time. When optimising the working load, the downstream processes are also considered, so as to minimize the total penalty cost due to shutting down of the units serviced by the utility system. The proposed methodology combines the operation and design problems and optimizes them simultaneously.

Real-time Utility System Optimization

ABSTRACT At the 2002 IETC, we described the benefits of a site-wide utility system simulation model that can translate all in-plant process modifications to the resulting changes in fuel, water, and electricity at the factory fence to ensure that process Btus are accurately expressed in real dollars. Last year, we demonstrated how such a model could be upgraded to an optimizer to determine the most cost-effective way to operate specific equipment items wherever the operator is faced with choices as to which equipment items to run at any time, which fuel to use, etc. The last 12 months have seen a number of “second generation” optimizers delivered to clients in Europe, the Far East, and the Americas, operating in real time. These latest models employ sophisticated data validation and reconciliation techniques to account for incomplete or conflicting plant measurements. Web-enabled reports are generated and the results of the data reconciliation can even be fed back to the site data historian as corrected plant measurements. This approach will typically reduce day-to-day utility system operating costs by between 2 and 5 percent in addition to the long-term capital and operating cost savings already identified by using this type of model at the project planning and design stage.

Top-level Analysis of Site Utility Systems

This paper presents a practical approach to the analysis of industrial utility systems to determine the true value of steam savings and to identify improvements to the utility system. The analysis is carried out for existing utility systems in cases when potential energy saving projects to site processes are identified and evaluated. The potential benefits identified can be used to determine the economics of retrofitting the processes on a site to decrease their energy consumption. The approach builds on a robust optimization framework for industrial utility systems. A stepwise optimization procedure is applied to determine the true marginal price of steam at the different pressure levels and the constraints on steam savings imposed by the utility system. This allows the analysis of complex utility systems with automated tools for utility system optimization. Steam at a given level can have different values depending on how much is saved. This contradicts the common practice of attributing a single price to steam at a given level. The resulting top-level analysis provides a strategy for steam saving on total sites and modifications to the utility system.

Modelling and Optimization of Utility Systems

New methods have been developed for the modelling of steam turbines and gas turbines. In particular, the new models give a better description of part-load performance than previously published models. These models have been incorporated, along with those for the other elements of steam systems, into an overall model for utility cogeneration systems. The degrees of freedom of steam systems allow significant opportunity for cost reduction through optimization. This results from the complexity of the interactions within such systems, the continually changing demands on the system and the complexity of cost tariffs for power and fuel. This paper presents improved methods for both the simulation and optimization of utility systems based on the improved models. Using the method presented, significant cost savings can be made by optimizing the system operation at no capital cost. Case studies show a significant potential for cost savings.

The use of new SQP methods for the optimization of utility systems

An equation-oriented (EO) mathematical model for the optimization of utility systems has been developed. The model includes an accurate physical description of water/steam and air process streams. These together with EO models for the required unit operations were used to optimize two utility systems: a small model containing two steam turbines and a larger model of a combined heat and power plant. The applicability of the model was established using a novel SQP (Sequential Quadratic Programming) method which employs a ‘tolerance tube’ approach (Zoppke-Donaldson , A Tolerance-Tube Approach to Sequential Quadratic Programming with Applications, PhD thesis, 1995) to decide whether to accept a step from the QP subproblem. Comparison was then made with a further new penalty-free optimization method named filterSQP (Nonlinear Programming without a Penalty Function, 1997), which was used to solve the model containing two steam turbines. The tuning of both the model and the use of filterSQP were further explored with numerical experiments on the effect of scaling; comparison of dense and sparse versions of filterSQP; and different initial sizes for the trust region. Also, the correctness is demonstrated of the ‘shadow price’ given by the SQP code for the steam generation temperature. The results show that the sparse filterSQP solver works very efficiently and provides a basis for modelling and optimization of larger, industrial size problems which are reported in a companion paper.

Optimal operation of an ethylene plant utility system

The algorithm developed allows the selection of the pressure and temperature conditions of the high, medium and low pressure vapor headers and the deaerator pressure of an ethylene plant utility system. The utility system optimization can be done simultaneously with the ethylene plant optimization including four decision variables: Conversion and dilution ratio of the pyrolysis reactor, cracked-gas compressor inlet pressure and demethanizer column pressure. Their values are calculated, solving a Nonlinear Programming subproblem where the modeling equations of the utility system and the ethylene plant are considered. A rigorous simulation of the utility system is carried out using a water property prediction package. There is a strong integration between the ethylene plant and the utility system due to the generation of high steam pressure in the pyrolysis reactor or the use of residual gas as fuel gas in the boilers. The sensitivity of the profit function with respect to the ethylene and utility plant optimization variables is shown for different ethylene prices optimal solutions.