GAMS supported optimization and predictability study of a multi-objective adsorption process with conflicting regions of optimal operating conditions

In process systems engineering, it is critical to design an effective and optimized process in a short period with minimum experimental trials. However, improvement of some process variables may deteriorate some other criteria due to conflicting regions of factor interests for optimal solution in multi-objective optimization (MOO) processes. Here, the global optimization of an adsorption case study with conflicting optimal solutions based on multi-objective Response Surface Methodology (RSM) design is facilitated with the implementation of BARON solver based on General Algebraic Modeling System (GAMS) with identical factor variables, levels, and model equations. RSM suggested fifteen different optimum settings of which the validation is quite expensive and onerous, whereas GAMS suggested a single optimum setting which makes it more economically viable especially for large scale systems. In addition, the GAMS-based optimization provided more accurate and reliable results when experimentally validated as compared to the RSM-based solution.     

基于先验知识的化工过程全流程优化策略研究

结合过程的先验知识来决定决策变量的动力学方程,同时融合智能优化算法的某些特性,提出了基于先验知识的全流程优化策略。该方法在聚酯生产过程的实际应用表明,计算时间大大缩短,结果满足生产实际,便于调整操作条件,达到了节能降耗的效果。     

基于GA-PSO算法的烧结料场原料库存量优化

原料采购库存成本的约束是钢铁企业流动资金的制约瓶颈,针对钢铁企业烧结料场铁矿粉原料采购与消耗特点,以企业原料库存费用最小为目标建立了烧结料场铁矿粉原料库存量优化模型,提出一种基于遗传-粒子群算法的烧结料场铁矿粉库存量优化方法。同时,应用某钢铁企业360 m²烧结生产线的综合原料场实际生产数据进行仿真验证,结果表明,该模型可以反映该钢铁企业综合料场铁矿粉库存量的实际情况,采用的优化方法可以得到模型的最优解,为钢铁企业采购计划的制定提供决策支持。     

Conceptual design and optimization of chemical processes under uncertainty by two-stage programming

This contribution presents a method and a tool for modelling and optimizing process superstructures in the early phase of process design where the models of the processing units and other data are inaccurate. To adequately deal with this uncertainty, we employ a two-stage formulation where the operational parameters can be adapted to the realization of the uncertainty while the design parameters are the first-stage decisions. The uncertainty is represented by a set of discrete scenarios and the optimization problem is solved by stage decomposition. The approach is implemented in the computer tool FSOpt (Flow sheet Superstructure Optimization) FSOpt provides a flexible environment for the modelling of the unit operations and the generation of superstructures and algorithms for the translation of the superstructure into non-linear programming models.The approach is applied to two case studies, the hydroformylation of dodec-1-ene and the separation of an azeotropic mixture of water and formic acid.     

Parallel optimization by means of a Spectral-Projected-Gradient approach

The judicious exploitation of the inherent optimization capabilities of the Spectral-Projected-Gradient method (SPG) is proposed. SPG was implemented in order to achieve efficiency. The novel adjustments of the standard SPG algorithm showed that the parallel approach proves to be useful for optimization problems related to process systems engineering. Efficiency was achieved without having to relax the problems because the original model solutions were obtained in reasonable time.     

Global optimization methods for chemical process design: Deterministic and stochastic approaches

Process optimization often leads to nonconvex nonlinear programming problems, which may have multiple local optima. There are two major approaches to the identification of the global optimum: deterministic approach and stochastic approach. Algorithms based on the deterministic approach guarantee the global optimality of the obtained solution, but are usually applicable to small problems only. Algorithms based on the stochastic approach, which do not guarantee the global optimality, are applicable to large problems, but inefficient when nonlinear equality constraints are involved. This paper reviews representative deterministic and stochastic global optimization algorithms in order to evaluate their applicability to process design problems, which are generally large, and have many nonlinear equality constraints. Finally, modified stochastic methods are investigated, which use a deterministic local algorithm and a stochastic global algorithm together to be suitable for such problems. Partly presented at PSE Asia 2000 (December 6–8, Kyoto, Japan)     

Multiresponse optimization based on the desirability function for a pervaporation process for producing anhydrous ethanol

A pervaporation process for producing anhydrous ethanol from industrial ethyl alcohol (95% v/v) was performed with a commercial PVA/PAN membrane. A central composite rotatable experimental design together with response surface methodology was implemented for studying and modeling the influence of operating conditions in terms of the temperature and the flow rate of the feed on the pervaporation performance, namely, the permeate flux and the separation factor. To obtain a trade-off between the permeate flux and the separation factor, a method for simultaneous optimization of multiple responses based on an overall desirability function was used. The optimization resulted in a feed temperature of 66 °C and a feed flow rate of 42 L/h. These operating conditions are expected to respond with a permeate flux of 0.107 kg/m² h and a separation factor of 40, which correspond to a satisfactory overall desirability.     

An efficient latent variable optimization approach with stochastic constraints for complex industrial process

For complex chemical processes, process optimization is usually performed on causalmodels fromfirst principle models. When the mechanism models cannot be obtained easily, restricted model built by process data is used for dynamic process optimization. A new strategy is proposed for complex process optimization, in which latent variables are used as decision variables and statistics is used to describe constraints. As the constraint condition will be more complex by projecting the original variable to latent space, Hotelling T² statistics is introduced for constraint formulation in latent space. In this way, the constraint is simplified when the optimization is solved in low-dimensional space of latent variable. The validity of the methodology is illustrated in pH-level optimal control process and practical polypropylene grade transition process.     

Design of memetic algorithms for the efficient optimization of chemical process synthesis problems with structural restrictions

In Urselmann et al., 2011a, Urselmann et al., 2011b we presented a memetic algorithm (MA) for the design optimization of reactive distillation columns. The MA is a combination of a problem-specific evolutionary algorithm (EA) that optimizes the design variables and a mathematical programming (MP) method that solves the continuous sub-problems with fixed discrete decisions which are proposed by the EA to local optimality. In comparison to the usual superstructure formulation, the search space of the MA is significantly reduced without excluding feasible solutions. The algorithm computes many different local optima and can handle structural restrictions and discontinuous cost functions. In this contribution, a systematic procedure to modify the MA to solve more complex design problems is described and demonstrated using the example of a reactive distillation column with an optional side- or pre-reactor with structural restrictions on the number of streams. New concepts to handle connected and optional unit operations are proposed.     

Uncertainty quantification and global sensitivity analysis of complex chemical process using a generalized polynomial chaos approach

Uncertainties are ubiquitous and unavoidable in process design and modeling. Because they can significantly affect the safety, reliability and economic decisions, it is important to quantify these uncertainties and reflect their propagation effect to process design. This paper proposes the application of generalized polynomial chaos (gPC)-based approach for uncertainty quantification and sensitivity analysis of complex chemical processes. The gPC approach approximates the dependence of a process state or output on the process inputs and parameters through expansion on an orthogonal polynomial basis. All statistical information of the interested quantity (output) can be obtained from the surrogate gPC model. The proposed methodology was compared with the traditional Monte-Carlo and Quasi Monte-Carlo sampling-based approaches to illustrate its advantages in terms of the computational efficiency. The result showed that the gPC method reduces computational effort for uncertainty quantification of complex chemical processes with an acceptable accuracy. Furthermore, Sobol’s sensitivity indices to identify influential random inputs can be obtained directly from the surrogated gPC model, which in turn further reduces the required simulations remarkably. The framework developed in this study can be usefully applied to the robust design of complex processes under uncertainties.     

A two-level measurement-based dynamic optimization strategy for a bioreactor in penicillin fermentation process

One measurement-based dynamic optimization scheme can achieve optimality under uncertainties by tracking the necessary condition of optimality (NCO-tracking), with a basic assumption that the solution ...     

一种基于测量的双层动态优化策略及其在青霉素发酵过程中的应用(英文)

One measurement-based dynamic optimization scheme can achieve optimality under uncertainties by tracking the necessary condition of optimality (NCO-tracking), with a basic assumption that the solution model remains invariant in the presence of al kinds of uncertainties. This assumption is not satisfied in some cases and the stan-dard NCO-tracking scheme is infeasible. In this paper, a novel two-level NCO-tracking scheme is proposed to deal with this problem. A heuristic criterion is given for triggering outer level compensation procedure to update the solution model once any change is detected via online measurement and estimation. The standard NCO-tracking process is carried out at the inner level based on the updated solution model. The proposed approach is il ustrated via a bioreactor in penicil in fermentation process.     

A prototype simulation-based optimization approach to model feedstock development for chemical process industry

Incorporating non-traditional feedstocks, e.g., biomass, to chemical process industry (CPI) will require investments in research & development (R&D) and capacity expansions. The impact of these investments on the evolution of biomass to commodity chemicals (BTCC) system should be studied to ensure a cost-effective transition with acceptable risk levels. The BTCC system includes both exogenous, e.g., product demands (decision-independent) and endogenous, e.g., the change in technology cost with investment levels (decision-dependent) uncertainties. This paper presents a prototype simulation-based optimization (SIMOPT) approach to study the BTCC system evolution under exogenous and endogenous uncertainties, and provides a preliminary analysis of the impact of using three different sampling methods, i.e., Monte Carlo, Latin Hypercube, and Halton sequence, to generate the simulation runs on the computational cost of the SIMOPT approach. The results of a simplified case study suggest that annual demand increases is the dominant factor for the total cost of the BTCC system. The results also suggest that using Halton sequence as the sampling method yields the smallest number of samples, i.e., the least computational cost, to achieve a statistically significant solution.     

An integrated dimensionality reduction and surrogate optimization approach for plant-wide chemical process operation

With liquefied natural gas becoming increasingly prevalent as a flexible source of energy, the design and optimization of industrial refrigeration cycles becomes even more important. In this article, we propose an integrated surrogate modeling and optimization framework to model and optimize the complex CryoMan Cascade refrigeration cycle. Dimensionality reduction techniques are used to reduce the large number of process decision variables which are subsequently supplied to an array of Gaussian processes, modeling both the process objective as well as feasibility constraints. Through iterative resampling of the rigorous model, this data-driven surrogate is continually refined and subsequently optimized. This approach was not only able to improve on the results of directly optimizing the process flow sheet but also located the set of optimal operating conditions in only 2 h as opposed to the original 3 weeks, facilitating its use in the operational optimization and enhanced process design of large-scale industrial chemical systems.     

基于物元分析的过程工业报警优化

过程工业报警系统设定的报警点多且复杂,给监控生产带来了一定的困难.结合报警系统的特点和物元分析方法,构造各报警参数的物元模型,定义各报警参数与报警级别的关联函数,提出基于关联函数的权重分配改进,计算各报警参数与报警级别的综合关联度.在保证安全生产的前提下,根据综合关联度的大小对各报警参数进行优化选择,形成适合过程工业的报警优化方法.结合精对苯二甲酸溶剂脱水塔报警系统验证了该方法的有效性,基于物元分析的报警优化方法合理地降低了报警系统的报警量和报警频率,为报警管理和操作优化提供了新思路.     

Feasibility analysis of a single-sensor-based approach for source identification of hazardous chemical releases

Source identification is critical for emergency responses to hazardous chemical releases, especially sudden releases of toxic gases. The timely arrangement of multiple sensors at the scene of a sudden accident is difficult. To overcome this limitation, a two-step source identification method based on a single sensor was developed. In the first step, the measured concentration was transmitted to the computing platform.First, a preliminary estimation of the release source was calculated from recently detected concentrations. Then, the preliminary result was used to predict the concentrations and to assess whether more measurements were needed. This data processing was conducted by the computing platform. In the second step, a new objective monitoring point was transmitted to the detector for the measurement of additional concentrations. These two steps were conducted repeatedly until the estimation adequately represented the release source. The fixed and mobile single sensor results were analyzed, and a comparison to multi-sensor results was also conducted. The results show that single-sensor source identification is attainable with a sufficient number of observations, and the number of valid concentration observations is required to be no less than the number of unknown parameters. To best estimate the release source, the movement strategy of the single sensor was based on the possible release source and the hazard partition of the gas plume. It is highly recommended that the single-sensor source identification method be used in unexpected incidents due to its flexibility and timely response.     

基于LSNPE算法的化工过程故障检测

复杂化工过程通常具有多个操作模态,而且采集的数据不服从单一的高斯或非高斯分布。针对化工过程的多模态和复杂数据分布问题,将局部标准化（local standardized,LS）策略应用于邻域保持嵌入（neighborhood preserving embedding,NPE）算法,提出了一种新的基于局部标准化邻域保持嵌入（local standardized neighborhood preserving embedding,LSNPE）算法的故障检测方法。首先,使用LSNPE算法提取高维数据的低维子流形,进行维数约减,同时保持邻域结构不变。其次,通过特征空间中样本的局部离群因子（local outlier factor,LOF）构造监控统计量并确定其控制限。相较于监控多模态化工过程的多模型策略,提出的LSNPE方法不需要过程先验知识的支持,只需建立一个全局的监控模型。最后,通过数值仿真及Tennessee Eastman（TE）过程仿真研究验证了本文提出方法的有效性。     

基于TA-ConvBiLSTM的化工过程关键工艺参数预测

化工过程中,掌握关键工艺参数的变化趋势对于消除潜在波动、维持工况稳定作用巨大。然而,传统的浅层静态模型很难对非线性和动态性显著的复杂序列数据进行精准预测。针对上述难题,提出一种深度预测模型TA-ConvBiLSTM,将卷积神经网络(convolutional neural networks, CNN)和双向长短时记忆网络(bi-directional long short term memory, BiLSTM)集成到统一框架内,使其不仅能在每个时间步上自动挖掘高维变量间的隐含关联,更能横跨所有时间步自适应提取有用的深层时序特征。此外,引入时间注意力(temporal attention, TA)机制,为反映目标变化规律的重要信息增加权重,避免其因输入序列过长、深层特征太多而被掩盖。所提出方法的有效性在国内某延迟焦化装置炉管温度预测的案例中得到验证。