Operation Optimization System Research Articles

Data-Driven Modeling and Operation Optimization With Inherent Feature Extraction for Complex Industrial Processes

In response to the tenets of Industry 4.0, operation optimization in industrial processes has become a significant research topic. However, the uncertainties prevailing in the process pose challenges to production operations, especially the feedstock properties. In this work, the operation optimization study is performed on a distillation unit (DU), a typical plant in the industrial process. To enhance production performance, a modeling and operation optimization strategy based on feedstock property and production features is presented. One of the difficulties is how to uncover features from high-dimensional and imperfect data, where imperfect data refers to product quality data that is unavailable online. In the strategy, we inject the inherent characteristic of the process into the data-driven method to extract the feedstock property in a data-based and knowledge-oriented manner. Further, optimal feature representation and process modeling can be achieved by customizing the network structure. The operation optimization problem is formulated to adjust the top temperature of the distillation column (TTDC) to achieve satisfactory production under varying feedstock properties. Experimental results illustrate that the process model based on feedstock property and production features (PM-FP-PF) can better fit the physical process mechanism even based on incomplete information in industrial data. Industrial experiments have shown the proposed strategy has advanced generalization ability to the different feedstock properties. The proposed operation optimization strategy (OOS) improves the product qualification rate and has broad application prospects in industrial processes with similar features. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Industrial processes suffer from a variety of disturbances that interrupt the smooth operation of the system, such as varying feedstock properties. How to deal with them is the key to improve the product qualification rate. In this work, we propose a data-driven modeling and operation optimization framework to improve product quality under varying feedstock properties. The dynamic variation characteristics of the feedstock properties can be obtained by analyzing the physical properties of the production unit. This is used as a basis for representing and extracting feedstock properties in a data-driven way. Further, a process model based on feedstock property and production features (PM-FP-PF) is built to predict product quality. An operation optimization strategy with production capacity consideration is established. It can determine the optimal operation action required by the current system, mitigating the uncertainty of feedstock property. This operation optimization system has been applied to a distillation unit in the hydrofining process. The application results show that the process model achieves satisfactory estimation accuracy, and the operation optimization strategy has improved production performance.

Robust Scheduling Optimization Model for Combined Cooling, Heating and Power System in Industrial Parks

The multi energy complementary system is a new power energy technology Firstly, we studied renewable energy and load uncertainties of an operation optimization system, and established the industrial park energy system, which includes wind power, photovoltaic power, a combined cooling, heating and power system, and an energy storage tank. Secondly, given the renewable energy uncertainties of unit output and load, we introduced a robust multi-objective operation optimization method for industrial park energy supply systems while considering conservative system operation. Thirdly, we examined the synergetic and game relationship among multiple objectives. The particle swarm optimization algorithm is was used to optimize the system operation scheme, reduce the feasible domain, and improve the efficiency of the solution. Finally, the simulation results show that the operation optimization method effectively uses the demand response to optimize economic and environmental objectives and ensure the optimal operation efficiency of the system under multiple uncertainties.

Developing an Analytical Model and Computing Tool for Optimizing Lapping Operations of Flat Objects Made of Alloyed Steels.

Lapping is a finishing process where loose abrasive grains contained in a slurry are pressed against a workpiece to reduce its surface roughness. To perform a lapping operation, the user needs to set the values of the respective lapping conditions (e.g., pressure, depth of cut, the rotational speed of the pressing lap plate, and alike) based on some material properties of the workpiece, abrasive grains, and slurry, as well as on the desired surface roughness. Therefore, a mathematical model is needed that establishes the relationships among the abovementioned parameters. The mathematical model can be used to develop a lapping operation optimization system, as well. To this date, such a model and system are not available mainly because the relationships among lapping conditions, material properties of abrasive grains and slurry, and surface roughness are difficult to establish. This study solves this problem. It presents a mathematical model establishing the required relationships. It also presents a system developed based on the mathematical model. In addition, the efficacy of the system is also shown using a case study. This study thus helps systematize lapping operations in regard to real-world applications.

Adaptive Modeling and Operation Optimization for the Cold End System of Thermal Power Units based on Mechanism and Statistical Analysis

The operation optimization for the cold end system is an efficient means to improve the economy of steam turbine units. To compensate for the inadequacy of the traditional mechanism analysis utilized in obtaining actual operating characteristics of the cold end system, the prediction model of the exhaust pressure was established on the basis of mechanism analysis combined with data from the operation process. An online adaptive updating strategy was introduced to guarantee the modeling accuracy. A discrete model of the cooling tower outlet water temperature (CTOWT) was constructed based on the operation data partitioned into different groups according to the pump operating mode change (POMC). Combining the above two models, the coupled model of the cold end system was therefore obtained. A model-based operation optimization system was then implemented for the cold end system in a coal-fired power plant. Experimental trials authenticate that the optimization suggestions provided by the system can effectively enhance the benefit of power generation.

Integrated Optimization for the Automation Systems of Mineral Processing

The whole production line of hematite ore processing is composed of raw ore processing, shaft furnace roasting, grindings, and magnetic separation production phases. Their automation systems consist of the process control part and the operational optimization system. The target of the optimal operational control is to optimize the concerned operational indices, namely, the intermediate product quality, efficiency, and consumptions. The dynamics between the operational indices and the global production indices (i.e., the total concentration grade, metal recovery rate, production rate, beneficiation ratio, and costs) with month, day, and hour time scales changes in line with the variations of production conditions, composition of raw ore together with capability of equipment. These indices are difficult to measure online and as a result it is difficult to model accurately. Moreover, there are characteristics in terms of both interconnections and conflictions among these indices. This leads to isolated operation of individual automation systems for these processes and the optimization of global production indices for whole production line cannot be realized. This paper presents a novel problem description for the integrated optimization of the automation systems of mineral processing. For this purpose, the analysis is made on the difficulty of using the existing optimization methods-based decision making methods to obtain the integrated optimization of the automation systems. The integrated optimization strategy for the automation systems of mineral processes is proposed using our previously established target value optimization of global production indices , two time scales decomposition approach and target value optimization of operational indices. The proposed strategy aims at realizing the optimization of global production indices. Using real data from a mineral processing plant on hematite beneficiation process, relevant simulations, and real industrial experiments have been carried out. The obtained experimental results show the efficiency and effectiveness of the proposed strategy.

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JOGMEC surveyed technical trend of intelligent well completion on 2009. The general idea of tools for intelligent well technology and resent technical trend are described in this article. Intelligent well technology, which is known as “smart well technology” too, is to maximize the subject field value with optimization of the production operation based on the real-time down-hole monitoring and flow control. Typical intelligent well technology systems are represented to surface system and down-hole system. Surface systems are consisted by data transfer & management system, and operation optimization system. On the other hand, Down-hole systems are consisted by isolation tool, flow control tool, and monitoring tool ; those are focused on this article. For the recent technical trend, following topics are described ; typical application of intelligent well technology, reliability of component tools, and newly development tools.

Optimal Power Flows: Uses, Methods and Developments

Optimal power flows are basic tools for the secure and economic operation of Power Systems, but are presently applied in a few energy control centers only. The first part of this paper is dedicated to an analysis of possible statements and uses of this technique. New results providing consistency with daily power scheduling in such systems as hydro power systems are pointed out. In the second part, the main methods now available are reviewed, including parametric algorithms, with a critical analysis of the relevant performances for on-line applications. In the third part, present developments performed at Electricite de France are described. They aim at suppressing shortcomings existing in present methods. Some very new results have been obtained. These developments build up a consistent and secure operation optimization system, from daily power scheduling up to automatic control, with Optimal Power Flows as the basic tool. In particular, a new load flow technique with “implicit active reactive coupling”, improvements in speed of the optimal power flow and the inclusion of the latter into a closed loop secure economic dispatch are presented.

How a computerized corporate planning model of a multinational firm contributed to evaluate an acquisition

To quantify the take-over value of a business firm, classical cash flow projection techniques frequently fail because the business is evaluated as an independent alternative. In this paper we show how a computer-based corporate planning model may be used to evaluate a candidate for take-over by an acquiring multinational firm. The main part of the corporate planning system was an operational optimization system, searching the optimal plan of allocation of product markets to companies. In the introductory stage of the modelbuilding the optimization was based on a heuristic algorithm. During the evaluation of the synergistic advantages of the take-over of a small competitor, the heuristic was compared with a more sophisticated mixed integer linear programming model. The results of this comparison are reported.