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According to the differences in physical properties of Jurassic crude oil around Fukang depression, geochemical characteristics of oil are analyzed from five zones. The results show that di...

Electroplating industry annually produces numerous plated workpieces for U.S. pillar industries. However, it is also a major environment polluter in forms of wastewater, sludge, and spent solution. Thus, waste source reduction for the electroplating industry is a must. In practice, when productivity and energy use efficiency are also taken into account, the design and operation of an electroplating process become very complicated and need an in-depth study. In this paper, productivity maximization, energy saving, and freshwater/wastewater minimization are simultaneously addressed for the optimal design and operation of electroplating processes, which generates a triple-objective mixed-integer dynamic optimization (MIDO) model. The MIDO model is iteratively solved by a developed methodology to obtain the 3D Pareto frontier of the optimization problem, which provides important technical supports for the design and operation of electroplating processes. The efficacy has been demonstrated with a case study on an electroplating process.

Hoist scheduling, especially cyclic hoist scheduling (CHS), is used to maximize the manufacturing productivity of electroplating processes. Water-reuse network design (WRND) for the electroplating rinsing system targets the optimal water allocation, such that fresh water consumption and wastewater generation are minimized. Currently, there is still a lack of studies on integrating CHS and WRND technologies for electroplating manufacturing. In this paper, a multi-objective mixed-integer dynamic optimization (MIDO) model has been developed to integrate CHS and WRND technologies for simultaneous consideration of productivity and water use efficiency for environmentally benign electroplating. The orthogonal collocation method on finite elements is employed to convert the MIDO problem into a mixed-integer nonlinear programming (MINLP) problem. The efficacy of the methodology is demonstrated by solving a real electroplating example. It demonstrates that the computational methods of production scheduling, process design, and dynamic optimization can be effectively integrated to create economic and environmental win–win situations for the electroplating industry.

The cracking furnace system is crucial for an olefin plant. Its operation needs to follow a predefined schedule to process various feeds continuously, meanwhile conducting a periodically decoking operation for each furnace when its performance apparently decreases. In practice, because the feed supply changes dynamically, the routine furnace scheduling is better performed in a dynamic and reactive way, through which the furnace operations can be smartly rescheduled with respect to any delivery of new coming feeds. Thus, the feeds from the new delivery and the leftover inventories can be timely, feasibly, and optimally allocated to different furnaces for processing to obtain the maximum average net profit per time. Facing this challenge, this paper develops a new MINLP-based reactive scheduling strategy, which can dynamically generate reschedules based on the new feed deliveries, the leftover feeds, and current furnace operating conditions. It simultaneously addresses all the major scheduling issues of a c...

The LNG (liquefied natural gas) receiving terminal is an important component of the entire LNG value chain. The handling of unloading BOG (boil-off gas) during LNG regasification at LNG receiving terminals significantly influences the BOG flare emission and energy consumption. In this work, thermodynamic-analysis-based design and operations are simultaneously considered to recover BOG with the minimum total energy consumption, a goal of which is to provide a cost-effective flare minimization strategy at LNG receiving terminals. A rigorous simulation-based optimization model and its solution algorithm are developed based on an LNG regasification superstructure. Case studies are used to demonstrate the efficacy of the developed methodology. The presented general optimization model and thermodynamic analysis also provide fundamental understandings of the LNG regasification process that are valuable for industrial applications.

Cracking furnaces of ethylene plants are capable of processing multiple feeds to produce smaller hydrocarbon molecules, such as ethylene, propylene, and ethane. The best practice for handling the produced ethane is to recycle it as an internal feed and conduct the secondary cracking in a specific furnace. As cracking furnaces have to be periodically shut down for decoking, when multiple furnaces processing different feeds under various product values and manufacturing costs are considered, the operational scheduling for the entire furnace system should be optimized to achieve the best economic performance. In this paper, a new MINLP (mixed-integer nonlinear programming) model has been developed to optimize the operation of cracking furnace systems with the consideration of secondary ethane cracking. This model is more practical than the previous study and can simultaneously identify the allocation of feeds with their quantity, time, and sequence information for each cracking furnace. A case study has demo...

Ethylene plant start-ups generate huge amounts of off-spec products for flaring, which cause negative environmental and societal impacts, as well as tremendous raw material and energy losses that could be unitized to generate much more needed products. Thus, cost-effective start-up flare minimization strategies through proactive process design and operation are becoming more important and attractive to the industry. However, fundamental and quantitative studies on start-up flaring emissions are still lacking, such as (i) what kinds of emission species are contained in the flaring sources; (ii) how much of each emission source will be generated during one start-up; and (iii) what is the dynamic emission profile of each emission source with respect to the start-up time? In this paper, rigorous plant-wide dynamic simulations are employed to characterize flaring emission sources under different flare minimization strategies for an ethylene plant start-up. Deep insights of the emission source distribution and ...

An ethylene plant employs multiple cracking furnaces in parallel to convert various hydrocarbon feedstocks to smaller hydrocarbon molecules, mostly ethylene and propylene. The continuous operational performance of cracking furnaces gradually decays because of coke formation in the reaction coils, which requires each furnace to be periodically shut down for decoking. Given multiple feeds and different cracking furnaces as well as various product prices and manufacturing costs, the operational scheduling for the entire furnace system should be optimized to achieve the best economic performance. In this paper, a new MINLP (mixed-integer nonlinear programming) model has been developed to obtain cyclic scheduling strategies for cracking furnace systems. Compared to previous studies, the new model has more capabilities to address operation profitability of multiple feeds cracked in multiple furnaces. Meanwhile, it inherently avoids unpractical conditions such as simultaneous shutdown of multiple furnaces. Case studies demonstrate the efficacy of the developed methodology.

Flaring is crucial to chemical plant safety. However, excessive flaring, especially the intensive flaring during the chemical plant start-up operation, emits huge amounts of volatile organic compounds (VOCs) and highly reactive VOCs, which meanwhile results in tremendous industrial material and energy loss. Thus, the flare emission should be minimized if at all possible. This paper presents a general methodology on flare minimization for chemical plant start-up operations via plantwide dynamic simulation. The methodology starts with setup and validation of plantwide steady-state and dynamic simulation models. The validated dynamic model is then systematically transformed to the initial state of start-up and thereafter virtually run to check the plant start-up procedures. Any infeasible or risky scenarios will be fed back to plant engineers for operation improvement. The plantwide dynamic simulation provides an insight into process dynamic behaviors, which is crucial for the plant to minimize the flaring w...

Production planning under uncertainty is considered as one of the most important problems in plant-wide optimization. In this article, first, a stochastic programming model with uniform distribution assumption is developed for refinery production planning under demand uncertainty, and then a hybrid programming model incorporating the linear programming model with the stochastic programming one by a weight factor is proposed. Subsequently, piecewise linear approximation functions are derived and applied to solve the hybrid programming model under uniform distribution assumption. Case studies show that the linear approximation algorithm is effective to solve the hybrid programming model, along with an error ≤0.5% when the deviation/mean ≤20%. The simulation results indicate that the hybrid programming model with an appropriate weight factor (0.1-0.2) can effectively improve the optimal operational strategies under demand uncertainty, achieving higher profit than the linear programming model and the stochastic programming one with about 1.5% and 0.4% enhancement, respectively.