In pesticide manufacturing,the handling and storageof hazardouschemicals are routinely performed under extreme conditions. Demandvariability, particularly short-term and seasonal shifts, can exacerbatesafety risks by increasing the need for extended storage of dangerousintermediates. Temporal and seasonal fluctuations in product demandsas well as dynamic market prices of raw materials can greatly affectthe cost-effectiveness, operational efficiency, and safety of pesticideproduction. To address these challenges, we present a superstructureflowsheet optimization approach for the selection of optimal processingroutes to produce glyphosatethe world’s most widelyused herbicideunder varying feedstock costs and product demandprofiles. A hierarchical, bilevel superstructure representation allowsfor adjusting the fidelity of design while enabling simultaneous designand planning of pesticide production processes. We apply the approachfor designing a glyphosate plant for meeting time-varying demandsin the San Joaquin Valley of California. The results show that varyingdemand profiles significantly influence process design, affectingboth the selection of chemical routes and storage requirements. Additionally,dynamic feed pricing introduces complex trade-offs, highlighting theneed for an integrated approach toward future expansion to includeboth safety and economic considerations.

Abstract In this article, a generalized optimization framework is proposed for the synthesis of thermally coupled distillation systems within an equation‐oriented environment. The proposed framework consists of three components: an efficient superstructure representation, a novel mathematical formulation, and the associated solution algorithm, encompassing a broad range of alternatives. The mathematical model is developed using conditional statements to activate specific sets of equations, effectively addressing existing zero‐flow issues. The synthesis problem is formulated as a Mixed Integer Nonlinear Programming problem, which is optimized using our previously developed Feasible Path‐Based Branch and Bound method, coupled with an improved Sequential Quadratic Programming algorithm. The computational studies demonstrate that the proposed optimization framework successfully solves complex benchmark problems for separating zeotropic multicomponent mixtures within reasonable computational time with good convergence performance from easily selected starting points. The optimal configuration generated leads to a reduction in total annualized cost ranging from 3.5% to 45%.

Abstract Co‐location of power plants and desalination systems allows for a reduction in operational expense through energy integration. Furthermore, augmenting fossil‐based power plants with solar energy provides a means of reducing the carbon footprint of electricity generation. It is also critical to protect the combined energy–water system against internal and external risk factors to maintain a reliable supply of both electricity and water. Therefore, a systematic approach for assessing and mitigating risks is needed. Because of the complex water–energy interactions, a superstructure representation is created and a quantitative risk assessment is conducted to show potential risk factors that target specific sub‐systems. A surrogate model of the flexibility index analysis is built in order to optimize the superstructure for both cost and flexibility objectives. Finally, the generated design is simulated against disruption scenarios to obtain its resilience against various risk factors. This approach is applied to a case study on the Kuwait water–energy plant to show how the developed approach can help decision‐makers create operational strategies to protect against risk in a cost‐effective manner.

Optimization of water-energy-food nexus via an integrated system of solar-assisted desalination and farming

A novel two-step approach for multi-plant indirect HENs design

Identification of sustainable carbon capture and utilization (CCU) pathways using state-task network representation

Integrated membrane material design and system synthesis

An optimization framework for the design of reverse osmosis desalination plants under food-energy-water nexus considerations

Biorefineries provide economic, environmental, and social benefits towards sustainable development. Because of the relatively small size of typical biorefineries compared to oil and gas processes, it is necessary to evaluate the options of decentralized (or distributed) plants that are constructed near the biomass resources and product markets versus centralized (or consolidated) facilities that collect biomass from different regions and distribute the products to the markets, benefiting from the economy of scale but suffering from the additional transportation costs. The problem is further compounded when, in addition to the economic factors, environmental and safety aspects are considered. This work presents an integrated approach to the design of biorefining facilities while considering the centralized and decentralized options and the economic, environmental, and safety objectives. A superstructure representation is constructed to embed the various options of interest. A mathematical programming formulation is developed to transform the problem into an optimization problem. A new correlation is developed to estimate the capital cost of biorefineries and to facilitate the inclusion of the economic functions in the optimization program without committing to the type of technology or the size of the plant. A new metric called Total Process Risk is also introduced to evaluate the relative risk of the process. Life cycle analysis is applied to evaluate environmental emissions. The environmental and safety objectives are used to establish tradeoffs with the economic objectives. A case study is solved to illustrate the value and applicability of the proposed approach.

An alternative method for chemical process synthesis using a block‐based superstructure representation is proposed. The block‐based superstructure is a collection of blocks arranged in a two‐dimensional grid. The assignment of different equipment on blocks and the determination of their connectivity are performed using a mixed‐integer nonlinear formulation for automated flowsheet generation and optimization‐based process synthesis. Based on the special structure of the block representation, an efficient strategy is proposed to generate and successively refine feasible and optimized process flowsheets. Our approach is demonstrated using two process synthesis case studies adapted from the literature and one new process synthesis problem for methanol production from biogas © 2018 American Institute of Chemical Engineers AIChE J, 64: 3082–3100, 2018

The increasing demands for water and the dwindling resources of fresh water create a critical need for continually enhancing desalination capacities. This poses a challenge in distressed desalination network, with incessant water demand growth as the conventional approach of undertaking large expansion projects can lead to low utilization and, hence, low capital productivity. In addition to the option of retrofitting existing desalination units or installing additional grassroots units, there is an opportunity to include emerging modular desalination technologies. This paper develops the optimization framework for the capacity planning in distressed desalination networks considering the integration of conventional plants and emerging modular technologies, such as membrane distillation (MD), as a viable option for capacity expansion. The developed framework addresses the multiscale nature of the synthesis problem, as unit-specific decision variables are subject to optimization, as well as the multiperiod capacity planning of the system. A superstructure representation and optimization formulation are introduced to simultaneously optimize the staging and sizing of desalination units, as well as design and operating variables in the desalination network over a planning horizon. Additionally, a special case for multiperiod capacity planning in multiple effect distillation (MED) desalination systems is presented. An optimization approach is proposed to solve the mixed-integer nonlinear programming (MINLP) optimization problem, starting with the construction of a project-window interval, pre-optimization screening, modeling of screened configurations, intra-process design variables optimization, and finally, multiperiod flowsheet synthesis. A case study is solved to illustrate the usefulness of the proposed approach.

We present a model-based optimization approach to determine the configuration of a petroleum refinery for grassroots (new) or existing site that considers a large number of commercial technologies particularly for heavy oil processing of crude oil residue from an atmospheric distillation unit. First, we develop a superstructure representation for the refinery configuration to encompass all possible topology alternatives comprising 96 technologies and their interconnectivities. The superstructure is postulated by decomposing it to incorporate representative heavy oil processing scheme alternatives that center on the technologies for atmospheric residual hydrodesulfurization (ARDS), vacuum residual hydrodesulfurization (VRDS), and residual fluid catalytic cracking (RFCC). We formulate a mixed-integer linear program (MILP) based on the superstructure by devising logic propositions on design and structural specifications that represent these processing options to aid convergence to an optimal refinery configuration. A numerical example is illustrated to implement the proposed technique in which an equivalent of more than two million refinery plot plans is evaluated. To assess the applicability and value of the approach, we validate the results against the literature as well as compare with existing real-world refinery configurations. A main contribution of this work is to demonstrate how a mixed-integer programming approach can be applied to a large-scale petroleum refinery design problem with suitable approximations informed by practical considerations to obtain results with reasonable computational load.

Fuel gas network (FGN) synthesis is a systematic method for reducing fresh fuel consumption in a chemical plant. In this work, we address FGN synthesis problems using a block superstructure representation that was originally proposed for process design and intensification. The blocks interact with each other through direct flows that connect a block with its adjacent blocks and through jump flows that connect a block with all nonadjacent blocks. The blocks with external feed streams are viewed as fuel sources and the blocks with product streams are regarded as fuel sinks. An additional layer of blocks are added as pools when there exists intermediate operations among source and sink blocks. These blocks can be arranged in a I × J two-dimensional grid with I = 1 for problems without pools, or I = 2 for problems with pools. J is determined by the maximum number of pools/sinks. With this representation, we formulate FGN synthesis problem as a mixed-integer nonlinear (MINLP) formulation to optimally design a fuel gas network with minimal total annual cost. We revisit a literature case study on LNG plants to demonstrate the capability of the proposed approach.

In this work a new approach to address multivariable control structure (MCS) design for medium/large-scale processes is proposed. The classical MCS design methodologies rely on superstructure representations which define sequential and/or bilevel mixed-integer nonlinear programming (MINLP) problems. The main drawbacks of this kind of approach are the complexity of the required solution methods (stochastic/deterministic global search), the computational time, and the optimality of the solution when simplifications are made. Instead, this work shows that, by using the sum of squared deviations (SSD) as well as the net load evaluation (NLE) concepts, the control structure design problem can be formulated as a mixed-integer quadratic programming (MIQP) model with linear constraints, featuring both optimality and improved computational performance due to state-of-the-art solvers. The formulation is implemented in the GAMS environment using CPLEX as the selected solver and two typical case studies are presented...

This article first reviews recent developments in process synthesis and discusses some of the major challenges in the theory and practice in this area. Next, the article reviews key concepts in optimization-based conceptual design, namely superstructure representations, multilevel models, optimization methods, and modeling environments. A brief review of the synthesis of major subsystems and flowsheets is presented. Finally, the article closes with a critical assessment and future research challenges for the process synthesis area.

The conceptual design of a petroleum refinery that satisfies multiple economics and operating constraints is a highly complex task. Coupled with the ever‐rising cost of designing and constructing a new refinery and the increasing demand for energy and fuels, there is incentive to optimize the energy recovery and energy efficiency of such a facility. This work addresses the flowsheet optimization of the synthesis of a petroleum refinery to attain an optimal heat‐integrated configuration or topology. A sequential two‐step strategy is employed that first performs simultaneous flowsheet optimization and heat integration to obtain an optimal refinery topology with minimum utility cost. Subsequently, the fixed optimal topology with minimum utility loads is optimized to arrive at a configuration with the fewest heat exchanger units. A mixed‐integer linear program (MILP) is formulated based on a superstructure representation that considers many alternative feasible refinery topologies. The computational results show meaningful reduction in the total annualized capital and operating costs as compared to a non‐heat‐integrated configuration.

We present a framework for the efficient representation, generation, and modeling of superstructures for process synthesis. First, we develop a new representation based on three basic elements: units, ports, and conditioning streams. Second, we present four rules based on “minimal” and “feasible” component sets for the generation of simple superstructures containing all feasible embedded processes. Third, in terms of modeling, we develop a modular approach, and formulate models for each basic element. We also present a canonical form of element models using input/output variables and constrained/free variables. The proposed methods provide a coherent framework for superstructure‐based process synthesis, allowing efficient model generation and modification. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3199–3214, 2016

Synthesis of reverse osmosis desalination network under boron specifications

Optimization of a reactive distillation process with intermediate condensers for silane production

Thermodynamic design of a cascade refrigeration system of liquefied natural gas by applying mixed integer non-linear programming