PTQ Q2 2025 Issue

Enterprise-wide optimisation across hydrocarbon processing assets

Consider the layers of efficiency when establishing a global resource optimisation modelling system

Jun Yi, Pei Su and Yonglei Wang Galaxy Sky-grand Technology Co., Ltd. Weijun Yang PetroChina Jinxiang Mao Sinopec Ricky Hsu Independent Consultant

E nterprise optimisation problems can be multi- plant, multi-period, or a combination of both, as well as pipeline or production plans and schedules. The Global Resource Optimization Modeling System (GROMS), used to optimise large multi-refinery enterprise problems, h as been shown to increase profit potential by $ per ton ($0.25/barrel) of crude oil feed vs other commercially avail- able programs. Advanced characteristics GROMS uses a database platform with a basic structure defined by the ‘model business dictionary’. Users only need to define and manage a ‘business dictionary’. There is no need to define and manage ‘code dictionaries’ once defined for periods, companies, production equipment, processing cases, logistics, physical properties, and recursive variables. The GROMS math matrix is generated entirely by the mixed-integer nonlinear programming (MINLP) algorithm dictionary and can simultaneously detect the correctness of the data entered by the user. Model builders and users are focused on the business and do not need to clearly define all model structures for purchasing, primary and secondary processing, product mixing, and sales. There is also no need to be concerned about the relationship between these structures. The GROMS model structure was originally developed as a mathematical matrix for ‘multi-enterprise, multi-cycle, multi-business, and multi-goal’ business relationships. The MINLP algorithm, implemented in C++ language, directly operates the model matrix (in matrix product state [MPS] format) and automatically generates the initial value of the coefficients required for solving with a commercially available solver. The model system has been designed according to this architecture and systematically optimised for performance on 10 years of model experience. Larger problems can, there- fore, be solved faster with a low occurrence of local optimum. User interfaces The programming efficiently utilises a database platform with a unique and easy-to-use user interface to build models, manage case data input, and analyse results with user-customised reports.

The GROMS main interface (see Figure 1 ) includes: • Top menu: Selection area of ‘function menu, model filter conditions’. • Model tree (left column of Figure 1) : Display and maintain ‘model classification, logistics structure, logistics relation - ship’, and more. • Main data window: Secondary unit yields and controls, which generally display and maintain all constraint data, such as model logistics, integers, prices, physical proper- ties, operating conditions, and pipelines. The calculation interface (see Figure 2 ) includes: • Window 1: Select the model to be calculated. • Window 2: Select the CASE to be calculated and display the calculated target value and convergence status. • Window 3: Select the model ‘logistics, physical properties’ constraint group. Constraint groups that are not selected are not included in the calculation, resulting in convenient step-by-step commissioning. • Window 4: Set the parameters of the MIN3LP algorithm. When there is a computational problem or a local optimal solution, it can usually be solved by adjusting the calcula- tion parameters. • Middle toolbar: Function buttons such as calculation operations, result viewing, and report charts. GROMS quickly customises the output of the process flow diagram. • Lower window: Displays the calculation log information of the MIN3LP algorithm. The result browsing interface (see Figure 3 ) includes: • Select row: Select model, CASE, and comparison CASE to be browsed. • Toolbar: Filter information and operation buttons required for browsing functions. • Data window: Information on constraints and results for both the basic CASE and the comparison CASE. Model types The modelling options or types available are: • LP: Linear programming • MILP: Mixed integer linear programming Mixed integer (MIP) constraint types can be directly defined, and MIP constraint data can be entered, such as ‘start-stop sign’, ‘batch and the number of batches’, ‘threshold’, ‘sequence’,

PTQ Q2 2025