Optimal operation of utility systems in petrochemical plants (original) (raw)

Utility systems operation: Optimisation-based decision making

Applied Thermal Engineering, 2011

Utility systems provide heat and power to industrial sites. The importance of operating these systems in an optimal way has increased significantly due to the unstable and in the long term rising prices of fossil fuels as well as the need for reducing the greenhouse gas emissions. This paper presents an analysis of the problem for supporting operator decision-making under conditions of variable steam demands from the production processes on an industrial site. An optimisation model has been developed, where besides for running the utility system, also the costs associated with starting up the operating units have been modelled. The illustrative case study shows that accounting for the shutdowns and start-ups of utility operating units can bring significant cost savings.

Analytical optimisation of industrial systems and applications to refineries, petrochemicals

Chemical Engineering Science, 2004

A new method for optimising process networks is presented in this paper. The method uses economic analysis of existing systems based on the value analysis method derived by Sadhukhan (2002) as the basis to derive the optimum network design. Optimising a large scale industrial system (e.g. refineries, petrochemicals) where multiple processes, many material streams and a number of supporting systems (e.g. energy) are involved, is a difficult task to achieve. For such a case, a fundamental, practical and systematic methodology for detailed differential economic analysis (Sadhukhan et al, 2003) of an industrial system at any market and environmental condition can be very useful for achieving its optimal operation.

Water and wastewater minimization in a petrochemical industry through mathematical programming

Journal of Cleaner Production, 2018

Petrochemical industries have large water demands in their operations, such as distillation, extraction, washing processes and cooling systems. The present work discusses water and wastewater minimization in the production process of a Brazilian petrochemical industry, applying mass integration via mathematical programming. This research is a case study applied in industrial scale, using data from a petrochemical plant. Alternatives for water reuse were evaluated by analyzing water consumption and wastewater generation processes by using a superstructure and a nonlinear mathematical programming model. The concentration of chemical oxygen demand in effluents was considered as a limiting factor. The alternatives found in this study could lead to total economy of 280,320 m³/y of water, and 236,520 m³/y of wastewater that would no longer be generated. This volume would sufficiently supply 4,626 inhabitants for one year, considering the average water consumption per capita in Brazil, which is of 166 liters per capita per day. Using this process, potential benefits could be achieved with water reuse.

Optimization of the Design and Partial-Load Operation of Power Plants Using Mixed-Integer Nonlinear Programming

Energy Systems, 2009

This paper focuses on the optimization of the design and operation of combined heat and power plants (cogeneration plants). Due to the complexity of such an optimization task, conventional optimization methods consider only one operation point that is usually the full-load case. However, the frequent changes in demand lead to operation in several partial-load conditions. To guarantee a technically feasible and economically sound operation, we present a mathematical programming formulation of a model that considers the partial-load operation already in the design phase of the plant. This leads to a nonconvex mixed-integer nonlinear program (MINLP) due to discrete decisions in the design phase and discrete variables and nonlinear equations describing the thermodynamic status and behavior of the plant. The model is solved using an extended Branch and Cut algorithm that is implemented in the solver LaGO. We describe conventional optimization approaches and show that without consideration of different operation points, a flexible operation of the plant may be impossible. Further, we address the problem associated with the uncertain cost functions for plant components.

Optimal Design of Petroleum Refinery Configuration Using a Model-Based Mixed-Integer Programming Approach with Practical Approximation

Industrial & Engineering Chemistry Research, 2018

We present a model-based optimization approach to determine the configuration of a petroleum refinery for grassroots (new) or existing site that considers a large number of commercial technologies particularly for heavy oil processing of crude oil residue from an atmospheric distillation unit. First, we develop a superstructure representation for the refinery configuration to encompass all possible topology alternatives comprising 96 technologies and their interconnectivities. The superstructure is postulated by decomposing it to incorporate representative heavy oil processing scheme alternatives that center on the technologies for atmospheric residual hydrodesulfurization (ARDS), vacuum residual hydrodesulfurization (VRDS), and residual fluid catalytic cracking (RFCC). We formulate a mixed-integer linear

Modeling Analysis and Optimization of Process.and Energy Systems

Energy costs affect the profitability of virtually every process. This book provides a unified platform for process improvement through the analysis of both the energy demand side—the processing plant—and the energy supply side— available heat and power resources. Emphasis is placed on first quantifying the material and energy flows in a process. The energy needs of the process guide the optimal design of the utility system. Techniques are also presented to ensure that the most cost-effective operation of the utility system is maintained

An Milp Process Optimization Model For A Petrochemical Complex

2003

This paper develops a multiperiod mixed integer nonlinear programming (MINLP) model for planning the production and operation of a real world petrochemical complex. Solving the MINLP model directly results in inconsistency in solution quality and time. Therefore, a reformulation and linearization technique is first applied to the bilinear terms of the model to obtain an mixed integer linear programming (MILP) formulation which is the initial point to solve the NLP obtained by the reformulation of the MINLP. The model is solved with GAMS 2.25 using OSL and CONOPT2. Two case scenarios are shown to illustrate the scope of this model.

A Mathematical Programming Model for Optimum Economic Planning of the Saudi Arabian Petrochemical Industry

A new mixed integer linear programming model is formulated and used to model the development of the petrochemical industry in Saudi Arabia. The proposed model features a new mathematical programming formulation, new products and processes, new variables and constraints, and more accurate estimates of production costs based on local conditions. The products considered in the model are classified into four main categories: aromatics, ethylene derivatives, propylene derivatives, and synthetic gas derivatives. The model is used to recommend petrochemical products, their respective capacities, and the corresponding production technologies. Utilization of results is discussed and sensitivity analysis is performed.

Various approaches in optimization of a typical pressurized water reactor power plant

Applied Energy, 2009

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