A systematic approach for synthesizing a low-temperature distillation system

In this paper, by combining a stochastic optimization method with a refrigeration shaft work targeting method, an approach for the synthesis of a heat integrated complex distillation system in a low-temperature process is presented. The synthesis problem is formulated as a mixed-integer nonlinear programming (MINLP) problem, which is solved by simulated annealing algorithm under a random procedure to explore the optimal operating parameters and the distillation sequence structure. The shaft work targeting method is used to evaluate the minimum energy cost of the corresponding separation system during the optimization without any need for a detailed design for the heat exchanger network (HEN) and the refrigeration system (RS). The method presented in the paper can dramatical y reduce the scale and complexity of the problem. A case study of ethylene cold-end separation is used to il ustrate the application of the approach. Compared with the original industrial scheme, the result is encouraging.     

Energy demand management for process systems through production scheduling and control

Demand response (DR) is an integral part of the Smart Grid paradigm, and has become the focus of growing research, development, and deployment in residential, commercial and industrial systems over the last few years. In process systems, energy demand management through production scheduling is an increasingly important tool that has the potential to provide significant economic and operational benefits by promoting the responsiveness of the process operation and its interactions with the utility providers. However, the dynamic behavior of the underlying process, especially during process transitions, is seldom taken into account as part of the DR problem formulation. Furthermore, the incorporation of energy constraints related to electricity pricing and energy resource availability presents an additional challenge. The goal of this study is to present a novel optimization formulation for energy demand management in process systems that accounts explicitly for transition behaviors and costs, subject to time‐sensitive electricity prices and uncertainties in renewable energy resources. The proposed formulation brings together production scheduling and closed‐loop control, and is realized through a real‐time or receding‐horizon optimization framework depending on the underlying operational scenarios. The dynamic formulation is cast as a mixed‐integer nonlinear programming problem based on a proposed discretization approach, and its merits are demonstrated using a simulated continuous stirred tank reactor where the energy required is assumed to be roughly proportional to the material flow. © 2015 American Institute of Chemical Engineers AIChE J, 61: 3756–3769, 2015     

Economic optimization of the water reuse network in batch process industries

Water reuse has proved to be an efficient way to reduce freshwater demand and wastewater generation in process industries. In this paper, a methodology for minimizing the costs associated to water management in batch process industries is presented. This work is based on a previous methodology for freshwater demand minimization, which has been extended to the economical aspects of water management: cost associated to freshwater supply and conditioning, wastewater treatment and disposal, as well as water reuse network investment and operation costs. Water reuse network design is optimized from the economical point of view, minimizing the operation and investment costs associated to the water use.     

考虑原油性质波动的炼厂氢气网络集成优化

针对原油性质的不确定性,提出了一种基于质量传递机理的随机规划建模框架,以实现炼厂氢气网络在经济效益和抗扰能力上的同步优化。该框架耦合了常减压蒸馏、加氢精制以及闪蒸分离等过程单元,从微观上解析原油性质波动对网络运行的影响;采用了代理模型技术增设脱硫模块,并利用了二阶段随机规划方法改造管网,从宏观上优化氢气网络以满足生产要求。为验证所提方法的有效性和适用性,对某一现有的炼厂氢网络进行了改造设计研究。结果表明,集成过程单元的多场景优化策略能够有效提升网络的经济性能,并且能使其灵活应对因原油性质波动引起的操作场景的改变。     

Heat exchanger network design of large-scale industrial site with layout inspired constraints

This paper presents a systematic approach for the synthesis of the heat recovery network in total site using a Mixed Integer Linear Programming model. This model returns a near-to-optimal network configuration with minimum utility cost while allows to select geographically closest matches. The Heat Load Distribution is the subproblem of the network design and has been reported to be quite expensive to solve for large-scale problems. The computational complexity of HLD resides in the number of streams and the feasible networks. An additional challenge, raising particularly in industrial problems, has been the intermediate heat transfer network which aggravates the combinatorial complexity. The presented methodology deals with those difficulties by priority consideration based on the location of process units. It helps significantly reducing the computational time and also comes with a realistic network sketch with respect to the plant layout. Several examples are discussed along with a real industrial case study.     

Design of resilient processing plants—II Design and control of energy management systems

The “standard heat exchanger network problem” has been surprisingly difficult to solve. It is only now that simple and reliable methods have evolved to synthesize the most efficient network of heat exchangers to heat and cool known process streams between stated temperature bounds. This has taken over a decade of research and scores of publications.The “resilient heat exchanger network problem” requires a solution that can cope with the uncertainties of industrial design. Fixed flow rates and temperature bounds rarely occur industrially. Rather, an industrial heat exchanger network problem necessarily involves ranges of flow rates and ranges of temperature bounds, and must include ease of operation and control.In this paper we make several fundamental advances in the design of resilient heat exchanger networks. (1) An efficient design procedure is developed to yield resilient designs which handle fluctuations within the condition of maximum energy efficiency. (2) A control structure and operating policy are developed to adjust flow distributions in the network to meet temperature constraints with minimum utility usage.These developments are based on several new theorems concerning resiliency in network design.     

Expected energy analysis for industrial process planning problem with fuzzy time parameters

Industrial process planning is to make an optimal decision in terms of resource allocation. The planning objective can be to minimize the time required to complete a task, maximize customer satisfaction by completing orders in a timely fashion and minimize the cost required to complete a task. Based on time and energy consumption in an industrial process planning problem, a novel energy analysis method is proposed to solve it. According to different constraints and credibility theory, typical expected value models of energy for it are presented. In addition, a hybrid intelligent optimization algorithm integrating fuzzy simulation, neural network and genetic algorithm is provided for solving the proposed expected value models. Some numerical examples are also given to illustrate the proposed concepts and the effectiveness of the used algorithm.     

Optimal quality control of baker's yeast drying in large scale batch fluidized bed

Optimal quality control of drying process of baker's yeast in large scale batch fluidized bed dryer is presented using neural network based models and modified genetic algorithm (GA). The objective of this study is to determine optimal conditions to maximize product quality while minimizing energy consumption. For this purpose, the drying process and quality models based on neural network with delay units are combined for predicting the dry matter, product temperature, change in dry matter and the quality loss while minimizing energy consumption and this model is then used for optimal quality control. A stochastic method based optimization structure is designed in order to solve the optimization problem whose the objective function is discontinuous, non-differentiable, complex and highly non-linear. The results obtained by optimal quality control based on modified GA showed that the performance of the existing industrial scale drying process was improved. The constructed optimal quality control structure is very convenient for the production process applications and may be applied without too much modification.     

Evaluating demand response opportunities for power systems resilience using MILP and MINLP Formulations

While peak shaving is commonly used to reduce power costs, chemical process facilities that can reduce power consumption on demand during emergencies (e.g., extreme weather events) bring additional value through improved resilience. For process facilities to effectively negotiate demand response (DR) contracts and make investment decisions regarding flexibility, they need to quantify their additional value to the grid. We present a grid-centric mixed-integer stochastic programming framework to determine the value of DR for improving grid resilience in place of capital investments that can be cost prohibitive for system operators. We formulate problems using both a linear approximation and a nonlinear alternating current power flow model. Our numerical results with both models demonstrate that DR can be used to reduce the capital investment necessary for resilience, increasing the value that chemical process facilities bring through DR. However, the linearized model often underestimates the amount of DR needed in our case studies. Published 2018. This article is a U.S. Government work and is in the public domain in the USA. AIChE J, 65: e16508, 2019     

Flexible and economical operation of chlor-alkali process with subsequent polyvinyl chloride production

Demand response (DR) can compensate for imbalances in variable renewable energy supplies. This possibility is particularly interesting for electrochemical processes, due to their high energy intensity. To determine the technical feasibility and economic viability of DR, we chose the chlor-alkali process with subsequent polyvinyl chloride production, including intermediate storage for ethylene dichloride. We estimate the maximum possible cost savings of implementing load flexibility measures. A process model is set up to determine the system characteristic. Subsequent optimizations result in the facility's best possible dispatch depending on additional and minimum power load, storage volume, and cost of a load change. Real plant data are used to specify model parameters and validate the system characteristic and the plant dispatch. An economic evaluation reveals the economic advantages of efficiency and flexibility. The approach can be used to analyze the DR potential of other chlorine value chains or facilities with high electricity demand in general.     

Evaluating the demand response potential of ammonia plants

Demand-side management/demand response (DSM/DR) are key strategies for mitigating the inherent variability in electricity generation rates by renewable sources. This article represents—to our knowledge—the first foray into assessing the DR potential of ammonia plants. Ammonia plants are interesting candidates for DR initiatives because of their significant electricity use (for operating compressors driving the synthesis loop) and the ability to store the ammonia product relatively easily and safely. Our approach is based on formulating and solving an optimal DR scheduling problem for an ammonia plant while accounting for the process dynamics. To this end, we introduce a new Hammerstein–Wiener-inspired modeling framework based on injecting linear dynamics in a first-principles static nonlinear model of the process. The results are encouraging; for the cases considered, peak-time power consumption decreases between 3.57% and 7.40%, coupled with 1.39% to 3.70% reductions in operating cost.     

Modification and optimization of benzene alkylation process for production of ethylbenzene

In this paper, an industrial ethylbenzene production unit has been simulated and the results are compared against five-day experimental data. According to prevailing unit condition, i.e. recycled ratio of benzene, benzene selectivity, and energy consumption, the unit is not working under its optimum conditions for minimum cost of ethylbenzene production. In the current design, high amount of benzene recycle (6:1) causes to have an additional cost due to fractionation of ethylbenzene from benzene. A new approach is proposed to modify the benzene alkylation process and reduce the unit's energy consumption. In the newly designed scheme, two double-bed alkylation reactors converted into four single-bed reactors. The amount of injected ethylene, alkylation reactors temperature, and recycled stream are regulated as adjustable parameters for the optimization of the process. In the modified process, the reflux ratio reduced to 1.87 and the benzene selectivity increased. The optimized process shows a considerable decrease in the unit's energy consumption in compare to the current process. Also, the mass fraction of ethylbenzene would reach to 99.12% of purity before entering to the transalkylation reactor for further purification. Therefore, if the presented purity is acceptable for the final application, the transalkylation reactor could be eliminated from the new design.     

Solar desalination using the vacuum freezing ejector absorption (VFEA) process

The vacuum freezing ejector absorption (VFEA) process is a freezing process in which the vapor is compressed by a combination steam ejector and absorber loop. The primary energy source is thermal energy to be provided by an array of solar collectors. In this paper, a detailed mathematical analysis of the combined VFEA and solar systems is presented. A program for design optimization by digital computer has been developed that searches over possible design parameters to find the design corresponding to minimum cost of water. Results of the program indicate the effect of the main design parameters and local solar and weather conditions on the resulting cost of water.     

Modular supply chain optimization considering demand uncertainty to manage risk

Supply chain under demand uncertainty has been a challenging problem due to increased competition and market volatility in modern markets. Flexibility in planning decisions makes modular manufacturing a promising way to address this problem. In this work, the problem of multiperiod process and supply chain network design is considered under demand uncertainty. A mixed integer two-stage stochastic programming problem is formulated with integer variables indicating the process design and continuous variables to represent the material flow in the supply chain. The problem is solved using a rolling horizon approach. Benders decomposition is used to reduce the computational complexity of the optimization problem. To promote risk-averse decisions, a downside risk measure is incorporated in the model. The results demonstrate the several advantages of modular designs in meeting product demands. A pareto-optimal curve for minimizing the objectives of expected cost and downside risk is obtained.     

Energy conservation in an optimized electrochemical process

In industrial electrochemical process, cost resulting from energy consumption and investment are balanced by operating at an optimum current density. Energy conservation measures such as technical process improvement or changes in energy prices often result in new optimum operating conditions. This has to be considered in assessing the effect of suh measures. The inter-relationship between process operating conditions, product cost, and energy consumptions have been studied by means of model calculations     

Designing sustainable alternatives for batch operations using an intelligent simulation–optimization framework

The drive towards sustainability has compelled the batch process industries to implement the concept of environmentally friendly plants. However, the temporal nature of processing in these processes obviates the application of traditional waste minimization, material recycling, or energy integration schemes. Further, most of the existing methodologies for generating sustainable alternatives are restricted to specific problems, such as reaction byproduct, wastewater, or solvent minimization. In this paper, we propose an intelligent simulation–optimization framework for identifying comprehensive sustainable alternatives for batch processes. We differentiate between wastes generated by the reaction–separation process and cleaning wastes. A P-graph-based approach is used for identifying the root cause of process waste generation and generating broad design alternatives. Specific variable-level design solutions are then identified and evaluated using process simulation. The cleaning wastes resulting from the optimized process are also minimized using a source-sink allocation method that allows design of recycle network structure. A multi-objective stochastic optimization method is used to integrate the analysis so that the overall process economic and environmental footprint is optimized. We illustrate the proposed methodology using a well-known literature case study involving reaction, distillation and washing operation.     

Optimal design for flexible operation of the post-combustion CO2 capture plant with uncertain economic factors

The ultimate benefit of flexible operation of the post-combustion CO₂ capture (PCC) plant depends on the ability to optimally balance between many competing factors, including the additional capital investment and operating cost savings. In this work, a large number of scenarios are constructed by considering combinations of possible realizations of the uncertain economic factors such as energy cost profile, emission penalty and value of captured CO₂. Then, the design choices like the size of the storage tanks and the regeneration capacity are optimized by minimizing an overall cost averaged over all the scenarios. The optimal design problem is naturally formulated as a two-stage stochastic program. This multi-scenario optimal design is compared with the design that minimizes the overall cost for just a single nominal scenario as well as the design that minimizes the cost averaged over the worst-case scenarios.     

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.     

Simultaneous optimization approach for integrated water-allocation and heat-exchange networks

A systematic design methodology is developed in this work for simultaneously synthesizing the multi-contaminant water-allocation and heat exchange network (WAHEN) in any chemical process. Specifically, a modified state-space representation is adopted to capture the structural characteristics of the integrated WAHEN, and a mixed-integer nonlinear program (MINLP) is formulated accordingly to minimize the total annualized cost (TAC) of the network design. In the proposed mathematical programming model, not only all possible water reuse and treatment options are incorporated, but also the direct and indirect heat-exchange opportunities are considered as well. To enhance the solution quality and efficiency, a stochastic perturbation procedure is introduced to generate reliable initial guesses for the deterministic optimization procedures and also, an interactive iteration method is developed to guide the search toward a potential global optimum. Three examples are presented in this paper to demonstrate the validity and advantages of the proposed approach.     

Design of flexible heat exchanger network for multi-period operation

Heat exchanger networks (HENs) increase heat recovery from industrial processes by matching hot and cold streams to exchange heat and reducing utility consumption. The design of HENs is a very complex task which generally involves mixed-integer non-linear programming (MINLP).This work evaluates and critically compares existing HEN design methods. It then presents a systematic methodology in the design of HENs under multiple periods of operation. The model presented in this work is a superstructure-based MINLP model which minimises the total annualised cost containing heat exchanger area cost and utility costs. The model is based on the superstructure by Yee and Grossmann [1990. Simultaneous optimisation models for heat integration—II, heat exchanger network synthesis. Computer & Chemical Engineering 14(10), 1165-1184], which was later formulated for multiple periods by Aaltola [2002. Simultaneous synthesis of flexible heat exchanger network. Applied Thermal Engineering 22, 907-918]. It includes a multi-period simultaneous MINLP model to design the HEN structure, and an NLP model to improve the solution and allow for non-isothermal mixing. Modifications to Aaltola's model include the use of maximum area per period in the area cost calculation of the MINLP objective function, and the removal of slack variables and weighed parameters from the existing NLP improvement model.The new model has been applied to one industrial case study, demonstrating that the new combined MINLP-NLP model can obtain better solutions by not relying on the average area assumption in the MINLP stage.