Optimization of a Process Synthesis Superstructure Using an Ant Colony Algorithm

The optimization of chemical syntheses based on superstructure modeling is a perfect way for achieving the optimal plant design. However, the combinatorial optimization problem arising from this method is very difficult to solve, particularly for the entire plant. Relevant literature has focused on the use of mathematical programming approaches. Some research has also been conducted based on meta‐heuristic algorithms. In this paper, two approaches are presented to optimize process synthesis superstructure. Firstly, mathematical formulation of a superstructure model is presented. Then, an ant colony algorithm is proposed for solving this nonlinear combinatorial problem. In order to ensure that all the constraints are satisfied, an adaptive, feasible bound for each variable is defined to limit the search space. Adaptation of these bounds is executed by the suggested bound updating rule. Finally, the capability of the proposed algorithm is compared with the conventional Branch and Bound method by a case study.     

Aeration control of a wastewater treatment plant using hybrid NMPC

In the operation of wastewater treatment plants a key variable is dissolved oxygen (DO) content in the bioreactors. As oxygen is consumed by the microorganisms, more oxygen has to be added to the water in order to comply with the required minimum dissolved oxygen concentration. This is done using a set of aerators working on/off that represents most of the plant energy consumption. In this paper a hybrid nonlinear predictive control algorithm is proposed, based on economic and control aims. Specifically, the controller minimizes the energy use while satisfying the time-varying oxygen demand of the plant and considering several operation constraints. A parameterization of the binary control signals in terms of occurrence time of events allows the optimization problem to be re-formulated as an nonlinear programming (NLP) problem at every sampling time. Realistic simulation results considering real perturbations data sets for the inlet variables are presented.     

Efficient multi-product multi-BOM batch scheduling for a petrochemical blending plant with a shared pipeline network

We present an effective scheduling heuristic for realistic production planning in a petrochemical blending plant. The considered model takes into account orders spanning a multi-product portfolio with multiple bills of materials per product, that need to be scheduled on shared production facilities including a complex pipeline network. Capacity constraints, intermediate storage restrictions, due dates, and the dedication of resources to specific product families have to be respected. The primary objective of the heuristic is to minimize the total order tardiness. Secondary objectives include the minimization of pipeline cleaning operations, the minimization of lead times, and the balanced utilization of filling units.The developed algorithm is based on a dynamic prioritization-based greedy search that schedules the orders sequentially. The proposed method can schedule short to mid-term operations and evaluate different plant configurations or production policies on a tactical level. We demonstrate its performance on various real-world inspired scenarios for different scheduling strategies.Our heuristic was used during the construction phase of a new blending plant and was instrumental in the optimal design of the plant.     

Automatic Identification of the Parameters of Heat Plants

The proposed algorithm for operational identification of heat plant parameters executed during use makes it possible to correct adjustment of the temperature regulator (including self-adjustment) in consideration of the current state of the plant. Automatic identification will increase the quality indexes for temperature regulation and also the quality of the manufactured product.     

A novel 3-layer mixed cultural evolutionary optimization framework for optimal operation of syngas production in a Texaco coal-water slurry gasifier☆

Optimizing operational parameters for syngas production of Texaco coal-water slurry gasifier studied in this paper is a complicated nonlinear constrained problem concerning 3 BP (Error Back Propagation) neural networks. To solve this model, a new 3-layer cultural evolving algorithm framework which has a population space, a medium space and a belief space is firstly conceived. Standard differential evolution algorithm (DE), genetic algorithm (GA), and parti-cle swarm optimization algorithm (PSO) are embedded in this framework to build 3-layer mixed cultural DE/GA/PSO (3LM-CDE, 3LM-CGA, and 3LM-CPSO) algorithms. The accuracy and efficiency of the proposed hybrid algo-rithms are firstly tested in 20 benchmark nonlinear constrained functions. Then, the operational optimization model for syngas production in a Texaco coal-water slurry gasifier of a real-world chemical plant is solved effective-ly. The simulation results are encouraging that the 3-layer cultural algorithm evolving framework suggests ways in which the performance of DE, GA, PSO and other population-based evolutionary algorithms (EAs) can be improved, and the optimal operational parameters based on 3LM-CDE algorithm of the syngas production in the Texaco coal-water slurry gasifier shows outstanding computing results than actual industry use and other algorithms.     

A MODEL-BASED DIRECT ADAPTIVE PI CONTROLLER FOR NONLINEAR PROCESS CONTROL

This article proposes a model-based direct adaptive proportional-integral (PI) controller for a class of nonlinear processes whose nominal model is input-output linearizable but may not be accurate enough to represent the actual process. The proposed direct adaptive PI controller is composed of two parts: the first is a linearizing feedback control law that is synthesized directly based on the process's nominal model and the second is an adaptive PI controller used to compensate for the model errors. An effective parameter-tuning algorithm is devised such that the proposed direct adaptive PI controller is able to achieve stable and robust control performance under uncertainties. To show the robust stability and performance of the direct adaptive PI control system, a rigorous analysis involving the use of a Lyapunov-based approach is presented. The effectiveness and applicability of the proposed PI control strategy are demonstrated by considering the time-dependent temperature trajectory tracking control of a batch reactor in the presence of plant/model mismatch, unanticipated periodic disturbances, and measurement noises. Furthermore, for use in an environment that lacks full-state measurements, the integration of a sliding observer with the proposed control scheme is suggested and investigated. Extensive simulation results reveal that the proposed model-based direct adaptive PI control strategy enables a highly nonlinear process to achieve robust control performance despite the existence of plant/model mismatch and diversified process uncertainties.     

基于流程模拟器和列队竞争算法的精馏操作优化

精馏是高耗能的操作单元,对其进行操作优化是企业实现节能降耗、提高经济效益的有效手段。本文提出列队竞争算法与流程模拟软件相结合的精馏操作优化方法,通过MATLAB-Aspen Plus的接口工具调用Aspen Plus模拟精馏操作过程,得到目标函数值,使用列队竞争算法对其进行操作条件的优化。将所提出的方法应用于某石化企业催化裂化主分馏塔的操作优化,其中目标函数值同时考虑了热能利用与产品分布,优化求解得到了总体效益最大的优化操作方案。     

Simulation of a supercritical carbon dioxide extraction plant with three extraction vessels

Although SuperCritical (SC) Fluid Extraction (SCFE) has been successfully applied commercially the last three decades, there is no systematic procedure or computational tool in the literature to scale-up and optimize it. This work proposes an algorithm to simulate dynamics in a multi-vessel (≥3) high-pressure SCFE plant where extraction vessels operate in batches, and is thus forced to use simulated-countercurrent flow configuration to improve efficiency. The algorithm is applied to a three-vessel SCFE plant using a shrinking-core model to describe inner mass transfer in the substrate. As example, the extraction of oil from pre-pressed seeds using SC CO₂ at 313 K and 30 MPa is simulated. After three cycles the process reaches a pseudo-steady-state condition that simplifies the estimation of plant productivity. Use of a three- instead of two-vessel SCFE plant increases oil concentration in the stream exiting the plant and decreases CO₂ usage at the expense of increasing extraction time.     

一种比例因子自调整的模糊预测函数控制

针对由主、副被控对象级联而成的大纯滞后系统,提出一种内回路采用PI控制、外回路采用具有比例因子自调整的模糊增量型预测函数控制策略,它将预测函数控制和具有比例因子自调整的模糊控制进行综合来共同控制由内回路和主被控对象构成的广义被控对象。大量的仿真实验表明:本方法较其他方法控制效果更好,即使在模型严重失配时,本方法仍具有很强的鲁棒性和抗干扰能力。     

Robust modeling of a microalgal heterotrophic fed-batch bioreactor

Microalgal feedstocks have shown potential for the production of biofuels and fine chemicals. Recently, an optimal experimental input profile for the identification of parameters of a microalgal bioreactor, containing 6 states and 12 unknown parameters has been proposed. In this work, the proposed design is implemented and parameters are estimated. It was found that the parameter estimation procedure can be made more computational efficient by the use of a novel iterative non-linear model reparameterization algorithm. By applying the proposed algorithm to experimental data, a good degree of output prediction was achieved along with bounds on the parameter values. The final, validated, model can be used for optimal control and process simulation.     

Modeling and simulation of energy distribution systems in a petrochemical plant

A systematic method for analysis and design of plant-wide energy distribution systems is proposed to minimize the net cost of providing energy to the plant. The method is based on the steady-state modeling and simulation of steam generation process and steam distribution network. Modeling of steam generation process and steam distribution network were performed based on actual plant operation data. Heuristic operational knowledges are incorporated in the modeling of steam distribution network. Newton’s iteration method and a simple linear programming algorithm were employed in the simulation. The letdown amount from superheated high-pressure steam (SS) header and the amount of SS produced at the boiler showed good agreement with those of actual operational data.     

Decentralized Fault Isolation by a Combination of Transfer Entropy and Classification Methods

Early fault detection and isolation in industrial systems is vitally necessary to prevent any potential product damage. The paper proposes a new decentralized multi-unit fault isolation methodology in which all the known process faults with similar time signatures are grouped into appropriate categories. An innovative genetic algorithm-based method is introduced to explore for optimum plant zones in a large-scale plant wide search to appropriately configure each architectural unit, having less reliance on excess process variables with redundant and uncorrelated diagnostic information. The methodology employs a set of Bayes and radial basis function neural network classifiers to properly isolate the most usual known faults. A new idea based on transfer entropy algorithm has been integrated in the decentralized configuration to be triggered for isolation of novel faults which have been left unrecognized by the set of maintained classifiers. Experimental results clearly demonstrate that the proposed methods are considerably superior to the conventional centralized methods.     

Optimal operation of the boil-off gas compression process using a boil-off rate model for LNG storage tanks

Proper handling of boil-off gas (BOG) significantly affects the operational efficiency as well as the safety of the whole LNG gasification plant. Due to the not well-known inherent dynamics, it has been suspected that the BOG compressors are being operated at too much capacity, unnecessarily consuming too much energy. An empirical model is proposed for the estimation of the boil-off rate (BOR) in an LNG storage tank, based on the specification supplied by the LNG storage tank manufacturer. By using the BOR model, an optimal operation algorithm is proposed for a safe and energy-saving BOG compressor operation, which minimizes the power consumption while preparing against the potential failure of one of the operating compressors. Case study results indicate that the energy consumption could be reduced by a half of the conventional method, by increasing the tank pressure while the safety is maintained. The proposed method is expected to be able to contribute to improving the efficiency of the whole gasification plant operation and control without tempering the safety requirements.     

HTR-PM大范围变负荷的MA自适应优化算法

为应对电网负荷需求的变化，球床模块式高温气冷堆核电站示范工程(HTR-PM)在设计上具备大范围变负荷运行以满足电网负荷的能力。由于无法获得大范围变负荷精确适用的模型，使得基于模型的操作优化面临挑战。为处理模型与对象的失配，提出HTR-PM大范围变负荷的MA(modifier adaptation)自适应优化算法。MA自适应优化算法利用过程反馈信息修正优化模型，促进优化模型与对象优化命题的一致性，进而有助于基于模型的操作优化收敛至对象的最优操作。借助信赖域框架，MA自适应优化算法可基于模型评价自适应更新模型、调整修正的优化模型的应用范围，确保在合适操作空间内求解优化模型。而且，信赖域框架还降低了算法性能对算法参数的敏感性。MA自适应优化算法在HTR-PM双堆同步大范围变负荷中的应用验证了方法的有效性。     

Flexibility study on a dual mode natural gas plant in operation

This work addresses a flexibility study on a natural gas processing plant through the integration of a process simulator to a "worst case" flexibility strategy. The plant has a gas subcooled turboexpansion design, which is suitable for working in dual operation mode; i.e., in either ethane production or ethane rejection mode. The selected uncertain parameters (feed flowrate, condensable hydrocarbons content, carbon dioxide content, and ambient temperature) have great impact on process operating conditions. The use of the worse case algorithm with the KS overestimation function for inequality constraints has also been explored to improve computational time, and numerical results are compared for both solution strategies. Results show, in terms of both robustness and speed of computation, that this approach can be a useful tool to complement operational analysis of large processing units, commonly performed by simulation and "what if" studies.     

Calculation of separation columns by nonlinear block successive relaxation methods

A novel technique: nonlinear block successive relaxation (NBSR) method, is proposed to solve the steady-state balance equations describing separation columns. The NBSR methods are a generalization of point relaxation methods frequently used towards solution of elliptic partial differential equations. Aspects of partitioning of the original set of equations into blocks as well as ordering of blocks is discussed. Problems arising in construction of a universal program based on the NBSR methods are presented. A new procedure making use of grouping of equations according to the set of trays is proposed. Aspects of calculation of separation equipments by this procedure is discussed. It is shown that overlapping blocks may essentially improve the convergence properties of the algorithm. The procedure is very simple and can be easily used towards calculation of large separation problems on a small computer. The method described is illustrated on calculation of an absorption column. Application of the NBSR methods to solution of a complex plant is suggested.     

BATCH-TO-BATCH OPTIMAL CONTROL OF BATCH PROCESSES BASED ON RECURSIVELY UPDATED NONLINEAR PARTIAL LEAST SQUARES MODELS

A batch-to-batch optimal control approach for batch processes based on batch-wise updated nonlinear partial least squares (NLPLS) models is presented in this article. To overcome the difficulty in developing mechanistic models for batch/semi-batch processes, a NLPLS model is developed to predict the final product quality from the batch control profile. Mismatch between the NLPLS model and the actual plant often exists due to low-quality training data or variations in process operating conditions. Thus, the optimal control profile calculated from a fixed NLPLS model may not be optimal when applied to the actual plant. To address this problem, a recursive nonlinear PLS (RNPLS) algorithm is proposed to update the NLPLS model using the information newly obtained after each batch run. The proposed algorithm is computationally efficient in that it updates the model using the current model parameters and data from the current batch. Then the new optimal control profile is recalculated from the updated model and implemented on the next batch. The procedure is repeated from batch to batch and, usually after several batches, the control profile will converge to the optimal one. The effectiveness of this method is demonstrated on a simulated batch polymerization process. Simulation results show that the proposed method achieves good performance, and the optimization with the proposed NLPLS model is more effective and stable than that with a batch-wise updated linear PLS model.     

BATCH-TO-BATCH OPTIMAL CONTROL OF BATCH PROCESSES BASED ON RECURSIVELY UPDATED NONLINEAR PARTIAL LEAST SQUARES MODELS

A batch-to-batch optimal control approach for batch processes based on batch-wise updated nonlinear partial least squares (NLPLS) models is presented in this article. To overcome the difficulty in developing mechanistic models for batch/semi-batch processes, a NLPLS model is developed to predict the final product quality from the batch control profile. Mismatch between the NLPLS model and the actual plant often exists due to low-quality training data or variations in process operating conditions. Thus, the optimal control profile calculated from a fixed NLPLS model may not be optimal when applied to the actual plant. To address this problem, a recursive nonlinear PLS (RNPLS) algorithm is proposed to update the NLPLS model using the information newly obtained after each batch run. The proposed algorithm is computationally efficient in that it updates the model using the current model parameters and data from the current batch. Then the new optimal control profile is recalculated from the updated model and implemented on the next batch. The procedure is repeated from batch to batch and, usually after several batches, the control profile will converge to the optimal one. The effectiveness of this method is demonstrated on a simulated batch polymerization process. Simulation results show that the proposed method achieves good performance, and the optimization with the proposed NLPLS model is more effective and stable than that with a batch-wise updated linear PLS model.     

An Improved Hybrid Genetic Algorithm for Chemical Plant Layout Optimization with Novel Non-overlapping and Toxic Gas Dispersion Constraints

New approaches for facility distribution in chemical plants are proposed including an improved non-overlapping constraint based on projection relationships of facilities and a novel toxic gas dispersion constraint. In consideration of the large number of variables in the plant layout model, our new method can significantly reduce the number of variables with their own projection relationships. Also, as toxic gas dispersion is a usual incident in a chemical plant, a simple approach to describe the gas leakage is proposed, which can clearly represent the constraints of potential emission source and sitting facilities. For solving the plant layout model, an improved genetic algorithm （GA） based on infeasible solution fix technique is proposed, which improves the globe search ability of GA. The case study and experiment show that a better layout plan can be obtained with our method, and the safety factors such as gas dispersion and minimum distances can be well handled in the solution.     

Minimization of the nonrenewable energy consumption in bioethanol production processes using a solar‐assisted steam generation system

The multiobjective optimization of a corn‐based bioethanol plant coupled with a solar‐assisted steam generation system with heat storage is described. Our approach relies on the combined use of process simulation, rigorous optimization tools, and economic and energetic plant analysis. The design task is posed as a bicriteria nonlinear programming problem that considers the simultaneous optimization of the plant profitability and the energy consumption. The capabilities of the proposed methodology are illustrated through a 120,000,000 kg/year corn‐based bioethanol plant considering weather data of Tarragona (Spain). © 2013 American Institute of Chemical Engineers AIChE J 60: 500–506, 2014