Optimal Reaction Research Articles

An air- and moisture-stable ruthenium precatalyst for diverse reactivity

Versatile, efficient and robust (pre)catalysts are pivotal in accelerating the discovery and optimization of chemical reactions, shaping diverse synthetic fields such as cross-coupling, C–H functionalization and polymer chemistry. Yet, their scarcity in certain domains has hindered the advancement and adoption of new applications. Here we present a highly reactive air- and moisture-stable ruthenium precatalyst [(tBuCN)5Ru(H2O)](BF4)2, featuring a key exchangeable water ligand. This versatile precatalyst drives an array of transformations, including late-stage C(sp2)–H arylation, primary/secondary alkylation, methylation, hydrogen/deuterium exchange, C(sp3)–H oxidation, alkene isomerization and oxidative cleavage, consistently outperforming conventionally used ruthenium (pre)catalysts. The generality and applicability of this precatalyst is exemplified through the potential for rapid screening and optimization of photocatalytic reactions with a suite of in situ generated ruthenium photocatalysts containing hitherto unknown complexes, and through the rapid discovery of reactivities previously unreported for ruthenium. The diverse applicability observed is suggestive of a generic platform for reaction simplification and accelerated synthetic discovery that will enable broader applicability and accessibility to state-of-the-art ruthenium catalysis.

Full-period operation optimization of the semi-regenerative reactor based on dehydrogenation reaction and deactivation kinetics

Semi-regenerative reactors are adopted in many catalytic processes. Based on industrial data, a one-dimensional pseudo-homogeneous reactor model was established for the semi-regenerative reactor of the dehydrogenation of heavy paraffins. The validation results show high accuracy of the model prediction. The Sequential Quadratic Programming (SQP) algorithm was employed for the full-period operation optimization of the reactor, resulting in a 3.04% increase in the yield of mono-olefins, but the optimized operation conditions are not consistent with common combination according to the practical production experience described in the literature. Further investigations for semi-regenerative reactors with different dehydrogenation systems show that the optimized combination of operation conditions in a full period is dependent on the depth of dehydrogenation, the rate of catalyst deactivation, and the duration of the period.

Increasing efficiency and treatment volumes for sonolysis of per- and poly-fluorinated substances, applied to aqueous film-forming foam

Sonolysis of per- and polyfluoroalkyl substances (PFAS) has recently matured to field studies, treating real world contamination. However, efficient sonolysis reactor designs are poorly researched. Moreover, the variety and complexity of PFAS pollution slows reactor optimisation and scale-up. In this work, the defluorination of 10.0 mg/L aqueous perfluorooctane sulfonic acid (PFOS) was used as a model metric for the optimisation of; reactor volume (0.6 or 1.4 L), power density (100 – 350 W L-1), number of modular reactors (1–3), and liquid height (56.7 – 340 mm). Note, the ultrasonic frequency (410 kHz) and flow rate (214.2 ml min−1) was optimised in this reactor previously. Peak PFOS defluorination rate (3.40 μmolL-1 min−1) occurred at 141.8 mm, in a 0.6 L reactor, under 200 WL–1 ultrasound. Increasing the number of transducers connected in parallel to one amplifier was able to increase treatment efficiency from 78.6 to 191.8 μmol kWh−1. The model was validated using legacy aqueous film forming foam (AFFF, 3 M FC-602 Lightwater) at different dilutions (×5, ×10, ×20 and ×100). Dilution played a role in AFFF sonolysis efficiency with optimal PFAS sonolysis rate (4.28 μmol L-1 min−1) at 20 × dilution. Overall AFFF was effectively modelled with a synthetic PFOS solution, attributed to limited matrix effects in AFFF sonolysis and high PFAS concentration (0.18–1.83 g L-1) dominated by PFOS (0.15 – 1.53 g L-1).

Exploring Gas-Phase Chemical Reactions in NH3/B2H6 Systems for Chemical Vapor Deposition Using Reactive Molecular Dynamics.

Hexagonal boron nitride (hBN) has attracted significant attention as a two-dimensional (2D) material due to its unique structure and properties. In this paper, we investigated the gas-phase reactions between B2H6 and NH3 and the reaction pathways potentially leading to the synthesis of hBN by using ReaxFF-based reactive molecular dynamics (MD) simulations. From the hundreds of chemical reaction pathways observed in these MD simulations, we extracted a highly reduced chemical kinetic model to describe the gas-phase mixture evolution in chemical vapor deposition of hBN using B2H6 and NH3 as precursors. The intent is to integrate this chemical model into future computational fluid dynamics (CFD) simulations of actual hBN deposition testing and production reactors to provide enhanced insights for experimental synthesis processes and reactor optimization. The chemical model of this study will serve as a stepping stone for large-scale simulations.

Leave No Photon Behind: Artificial Intelligence in Multiscale Physics of Photocatalyst and Photoreactor Design.

Although solar fuels photocatalysis offers the promise of converting carbon dioxide directly with sunlight as commercially scalable solutions have remained elusive over the past few decades, despite significant advancements in photocatalysis band-gap engineering and atomic site activity. The primary challenge lies not in the discovery of new catalyst materials, which are abundant, but in overcoming the bottlenecks related to material-photoreactor synergy. These factors include achieving photogeneration and charge-carrier recombination at reactive sites, utilizing high mass transfer efficiency supports, maximizing solar collection, and achieving uniform light distribution within a reactor. Addressing this multi-dimensional problem necessitates harnessing machine learning techniques to analyze real-world data from photoreactors and material properties. In this perspective, the challenges are outlined associated with each bottleneck factor, review relevant data analysis studies, and assess the requirements for developing a comprehensive solution that can unlock the full potential of solar fuels photocatalysis technology. Physics-informed machine learning (or Physics Neural Networks) may be the key to advancing this important area from disparate data towards optimal reactor solutions.

Multiphysics and multi-scale design and analysis of nuclear thermal propulsion pellet bed reactor based on spherical cermet fuel element

Due to the advantages of high thrust and high specific impulse, nuclear thermal propulsion (NTP) is a preferred near-term rocket engine technology for future Mars missions. This article introduces the reactor design based on spherical cermet fuel element, with the aim of studying the feasibility of applying this new fuel in NTP system. First, design requirements are determined based on the unique features of NTP. In terms of neutronics, the effect of rocket launch accidents and fuel loss caused by high-temperature hydrogen are considered. Thermal hydraulics refer to the requirements of NASA's Mars Design Reference Architecture 5.0 (DRA 5.0) for impulse, thrust, and thrust to weight ratio. Second, based on the requirements above, the optimal reactor configuration parameters are obtained with the goal of maximizing the thrust to weight ratio: reactor radius 51.1 cm, height 106 cm, aspect ratio 1.04, reflector 13 cm, and reactor mass 3940 kg. Finally, the performances of the optimal reactor design are analyzed. The neutron spectrum shows a characteristic of fast neutron spectrum, and fuel loss analysis shows that the core can withstand a fuel mass loss of about 4 wt%. In addition, the core axial and radial power distribution are calculated, with an overall power peak factor of 1.26. The thermal hydraulic calculation provides a range of core operating conditions that satisfy the design requirements of DRA 5.0 and temperature, with power of 620 ∼ 950 MW and flow rate of 16 ∼ 24 kg/s. Compared with the ANL200/2000 reactor using prismatic cermet, it has the advantage of higher specific impulse. The main disadvantage is the large pressure drop, which prevents it from being applied to the scenarios of high power and high thrust. Therefore, for NTP reactor using spherical cermet elements, it should be applied to low to medium power scenarios.

Direct reduction of iron-ore with hydrogen in fluidized beds: A coarse-grained CFD-DEM-IBM study

Hydrogen metallurgy technology uses hydrogen as the reducing agent instead of carbon reduction, which is one of the important ways to reduce carbon dioxide emissions and ensure the green and sustainable development of iron and steel industry. Due to the advantages of high gas-solid contact efficiency and outstanding mass and heat transfer, direct reduction of iron ore in fluidized beds has attracted much attention. Based on the three-layer unreacted shrinking core model and considering the diversity of particle structure during the reaction process, a multi-reaction path (multi-resistance network) model was established. In this study, a coarse-grained CFD-DEM-IBM solver based on hybrid CPU–GPU computing is developed to simulate the direct reduction process of two kinds of iron ore with hydrogen in fluidized beds, where the developed unreacted shrinking core model is used to model the reduction reactions, a coarse-grained model and multiple GPUs enable the significant acceleration of particle computation, and the immersed boundary method (IBM) enables the use of simple mesh even in complex geometries of reactors. The predicted results of particle reduction degree are in good agreement with the experimental values, which proves the correctness of the CFD-DEM-IBM solver. In addition, the effects of reaction kinetic parameters and operating temperature on particle reduction degree are also investigated. Present study provides a method for digital design, optimization and scale-up of ironmaking reactors.

Review of the research on the scrubbing of fission products in liquid metal pool

In recent times, the risk assessment and design optimization of advanced fourth-generation nuclear reactors have garnered significant global scholarly attention. Specifically, researchers worldwide have been focusing on the comprehensive analysis of the scrubbing process of radioactive fission product aerosols within the reactor containment pool under severe accident scenarios. This study delves into the historical progression of investigating the pool scrubbing phenomenon, which dates back to the mid-20th century. International efforts have encompassed various aspects, including assessing the migration behavior of radioactive nuclides within molten metal environments, conducting sensitivity analyses to gauge the impact of diverse variables on pool scrubbing, formulating models for analyzing the pool scrubbing process, and conducting relevant experimental validations. Drawing upon an extensive body of literature, this article presents a synthesized overview of research into the pool scrubbing process of fission product aerosols, focusing on three principal dimensions: model elucidation, experimental investigations, and computational simulations. The insights provided herein serve as a consolidated reference point for forthcoming endeavors in this domain of research.

Performance evaluation and optimization of a suspension-type reactor for use in heterogeneous catalytic ozonation

Packed fixed-bed reactors are traditionally used for heterogeneous catalytic ozonation. However, a high solid-to-liquid requirement, poor ozone dissolution, ineffective utilization of catalyst surface area, and production of large amounts of catalyst waste impede application of such reactors. In this study, we designed a suspension catalytic ozonation reactor and compared the performance of this reactor with that of a traditional fixed-bed catalytic ozonation reactor employing oxalic acid (OA) as the target contaminant. Our results showed that total O3 dissolved into the suspension reactor (117–134 mg.L−1) was much higher compared to that measured in the fixed-bed reactor (53 mg.L−1) due to a higher O3(g) interphase mass transfer rate in the suspension reactor. In accordance with the higher O3(g) interphase mass transfer, we observed a much higher proportional OA removal (32 %) compared to that achieved in the fixed-bed reactor (10%) employing an Fe-oxide catalyst supported on Al2O3 (Fe-oxide@Al2O3) in both reactors. Use of a double-layered Cu-Al hydroxide (Cu-Al LDHs) catalyst in the suspension reactor further enhanced the performance with nearly 90 % OA removal observed. Given the superior performance of the suspension reactor, we investigated the impact of operating conditions (catalyst dosage, hydraulic retention time and ozone dosage) employing Cu-Al LDHs as the catalyst. We also developed a mathematical kinetic model to describe the performance of the suspension reactor and, through use of the kinetic model, showed that O3(g) interphase transfer rate was the rate-limiting step in OA removal. Thus, improvement in ozone gas diffuser design is required to improve the performance of the suspension reactor. Overall, the present study demonstrated that suspension reactors were more effective than fixed-bed reactors for oxidation of surface-active organic compounds such as OA due to the higher ozone interphase mass transfer rate and effective utilization of the catalyst surface area that can be achieved. As such, further research on suspension reactor design and development of catalysts suitable for use in suspension reactors should facilitate large-scale application of catalytic ozonation processes by the wastewater treatment industry.

Design optimization of a cylindrical UV-C LED reactor for effective water disinfection with numerical simulations and test reactor fabrication

Ultraviolet C-band light-emitting diodes (UV-C LEDs) provide effective water disinfection and flexible reactor design. In this study, the disinfection performance of the cylindrical UV-C LED reactor was optimized using a new computational fluid dynamics (CFD) technique and validated experimentally with test reactors. The CFD technique provided UV doses for individual particles representing microorganisms, which were integrated along their trajectories using the discrete phase method within the radiation field acquired through the Monte Carlo method. Utilizing the minimum UV dose as a criterion, four key design parameters were evaluated: reactor aspect ratio (L/D) (in the range of 2.5−6), LED power angle (15−55°), wall reflectivity (0.6−0.94), and water flow rate (1−4 l/min). The optimal L/D of 4.2 was determined and subsequently applied to fabricate the test reactors. The polytetrafluoroethylene (PTFE) test reactor exhibited superior disinfection performance for E. coli O157:H7 compared to the one made of SUS304, due to its high wall reflectivity. Furthermore, a lower water flow rate enhanced disinfection performance by extending the residence time. The predicted log reductions exhibited a consistent trend with the measured values, confirming the efficacy of the CFD methodology applicable to various reactor designs and LED configurations.

Biomass: A Versatile Resource for Biofuel, Industrial, and Environmental Solution

Biomass, noted for its adaptability, has various applications in biofuel generation, industrial use, and environmental cleaning. This study looks into the multiple roles of biomass as a renewable energy source, with a particular emphasis on its vital contribution to biofuel production. Through a thorough evaluation of different conversion routes—thermal, biological, and physical—the study emphasizes thermochemical processes' efficiency, cost-effectiveness, and adaptability. Notably, technologies like gasification and quick pyrolysis are thoroughly investigated, followed by in-depth discussions of reactor optimization strategies to enhance performance and output. The complex structure of biomass, which is dominated by high-molecular-weight polysaccharides such as cellulose and hemicelluloses, demonstrates its significant potential for energy generation. Furthermore, the study categorizes biomass by content, origin, and conversion processes, resulting in a comprehensive inventory of available resources. Biomass from the agriculture and forestry industries, such as starch, sugar, lignocellulose, and organic wastes, is rigorously analyzed for energy production. Furthermore, various biomass processing techniques, including thermochemical, biochemical, and physicochemical conversions, are carefully tested in real-world applications to ensure their efficacy and viability. Beyond its importance in biofuel production, the article underlines biomass' versatility in satisfying industrial needs and contributing to environmental cleanup initiatives. This study lays the groundwork for informed decision-making and innovative solutions in various industries by providing a thorough understanding of biomass's various benefits and applications, including energy provision, industrial processes, and ecological restoration.

Strategies to achieve effective nitrogen activation

AbstractAmmonia serves as a crucial chemical raw material and hydrogen energy carrier. Aqueous electrocatalytic nitrogen reduction reaction (NRR), powered by renewable energy, has attracted tremendous interest during the past few years. Although some achievements have been revealed in aqueous NRR, significant challenges have also been identified. The activity and selectivity are fundamentally limited by nitrogen activation and competitive hydrogen evolution. This review focuses on the hurdles of nitrogen activation and delves into complementary strategies, including materials design and system optimization (reactor, electrolyte, and mediator). Then, it introduces advanced interdisciplinary technologies that have recently emerged for nitrogen activation using high‐energy physics such as plasma and triboelectrification. With a better understanding of the corresponding reaction mechanisms in the coming years, these technologies have the potential to be extended in further applications. This review provides further insight into the reaction mechanisms of selectivity and stability of different reaction systems. We then recommend a rigorous and detailed protocol for investigating NRR performance and also highlight several potential research directions in this exciting field, coupling with advanced interdisciplinary applications, in situ/operando characterizations, and theoretical calculations.

Optimizing biodiesel production from waste with computational chemistry, machine learning and policy insights: a review

AbstractThe excessive reliance on fossil fuels has resulted in an energy crisis, environmental pollution, and health problems, calling for alternative fuels such as biodiesel. Here, we review computational chemistry and machine learning for optimizing biodiesel production from waste. This article presents computational and machine learning techniques, biodiesel characteristics, transesterification, waste materials, and policies encouraging biodiesel production from waste. Computational techniques are applied to catalyst design and deactivation, reaction and reactor optimization, stability assessment, waste feedstock analysis, process scale-up, reaction mechanims, and molecular dynamics simulation. Waste feedstock comprise cooking oil, animal fat, vegetable oil, algae, fish waste, municipal solid waste and sewage sludge. Waste cooking oil represents about 10% of global biodiesel production, and restaurants alone produce over 1,000,000 m3 of waste vegetable oil annual. Microalgae produces 250 times more oil per acre than soybeans and 7–31 times more oil than palm oil. Transesterification of food waste lipids can produce biodiesel with a 100% yield. Sewage sludge represents a significant biomass waste that can contribute to renewable energy production.

Multiphase flow and reactor optimization of a 1MWth pilot-scale circulating fluidized bed for coal staged conversion

Coal-based cogeneration technology can realize the graded utilization of energy through the graded conversion of coal. In this work, the coal staged conversion in a 1MWth pilot-scale system was numerically studied using the multiphase particle-in-cell method which features gas–solid flow hydrodynamics, heat and mass transfer, homogeneous reactions, and heterogeneous reactions. The model was verified to be reliable and accurate in modeling coal staged conversion in fluidized bed reactors. Then the operating mechanism and particle behavior during the coal staged conversion process were comprehensively discussed. Additionally, the effects of operating and structural parameters on gas–solid mixing and chemical reactions in the reactor are explored for reactor optimization. The findings indicate that as the operating temperature increases, the CH4 concentration at the outlet of the gasifier’s cyclone initially rises before declining, whereas the trend for H2 concentration is inverse. As the particle size distribution (PSD) width becomes broader, the “core-annulus” structure of the expansion section in the dense-phase zone gradually disappears. Increasing the diameter of the expansion section in the dense-phase zone hinders the gas generation in the rare-phase zone. Increasing the diameter of the rare-phase zone significantly reduces the number of fine particles at the exit of the gasifier, and the height of the rare-phase zone can be appropriately lowered without leading to the overflow of fine particles. The change in structure size has little effect on the product gas yields at the outlet of the cyclone separator.

Unlocking the potential of sugarcane leaf waste for sustainable methane production: Insights from microbial pre-hydrolysis and reactor optimization

Sugarcane leaf waste, a byproduct of the growing global sugar industry, challenges agricultural waste management. This study explores its potential for methane production via anaerobic digestion. A microbial pre-hydrolysis, using lignocellulose-degrading bacteria, enhanced soluble chemical oxygen demand at an optimal initial substrate concentration of 40 g-volatile solid/L. Comparative analysis with untreated and bioaugmented leaves revealed the pre-hydrolyzed leaves achieved the highest methane production rate (MPR) at 14.0 ± 0.5 mL-CH4/L·d, surpassing others by 1.47 and 1.67 times. Two continuous stirred tank reactors were employed to assess the optimal hydraulic retention time (HRT). Results showed a stable methane production with an HRT of 25 days, yielding high MPRs: 88.70 ± 0.63 mL-CH4/L·d from pre-hydrolyzed sugarcane leaves and 82.57 ± 1.22 mL-CH4/L·d from microbial consortium-augmented leaves. A 25-day HRT fosters high microbial diversity with Bacteroidota, Firmicutes, Chloroflexi, and Verrucomicrobiota dominance, indicating favorable conditions. Conversely, a 20-day HRT results in lower diversity due to unfavorable factors like low pH during organic overloading, leading to increased concentrations of volatile fatty acids and lactic acid, with Firmicutes as the predominant phylum. This study highlights sugarcane leaf waste's potential as a valuable resource for sustainable methane production.

Improved Chemical Reaction Optimization With Fitness-Based Quasi-Reflection Method for Scheduling in Hybrid Cloud-Fog Environment

Improved Chemical Reaction Optimization With Fitness-Based Quasi-Reflection Method for Scheduling in Hybrid Cloud-Fog Environment

Emerging Sustainability in Carbon Capture and Use Strategies for V4 Countries via Biochemical Pathways: A Review

The world is moving towards decarbonization policies in the energy and industrial sectors to bring down carbon dioxide release and reach net zero emissions. Technologies to capture CO2 and use it as a feedstock to produce CO2-based chemicals and biofuels via chemical or biochemical conversion pathways can potentially reduce the amount of CO2 released. The paper serves the innovative scientific knowledge for CO2 transformation via a biochemical pathway to microalgal biomass with its subsequent treatment to biofuels and bioproducts assuming milder climatic conditions (Central or Eastern Europe, Visegrad countries or climatically related world regions). The recent trends were critically reviewed for microalgal biorefinery to reach the sustainability of microalgal-based chemicals with added value, digestion, hydrothermal liquefaction, pyrolysis, and gasification of microalgal residues. Knowledge-based chemical process engineering analysis, systematic data synthesis, and critical technical evaluation of available life cycle assessment studies evaluated the sustainability of microalgal biorefinery pathways. The research showed that biological CO2 fixation using water, seawater or wastewater to produce third-generation biomass is a promising alternative for bioethanol production via pretreatment, enzymatic hydrolysis, digestion, and distillation, and can be realized on a large scale in an economically viable and environmentally sound manner. Its best economically promising and sustainable pathway is perceived in producing microalgal-based nutraceuticals, bioactive medical products, and food products such as proteins, pigments, and vitamins. Machine learning methods for data mining, process control, process optimization, and geometrical configuration of reactors and bioreactors are the crucial research needs and challenges to implementing microalgal biorefinery in an operational environment.

Structural Optimization of Reactor for the Enhancement of CO2 Electrochemical Reduction Based on Gas–Liquid Mixing Flow Model

The CO2 electrochemical reduction reaction (CO2ERR) is heralded for carbon dioxide and renewable energy utilization. However, complexities in catalyst optimization and reactor structure research hinder its practical application. Herein, rather than traditional catalyst optimization, emphasis is placed on refining the reactor structure to enhance gas–liquid mixing. The goal is to raise the CO2 concentration in the reaction zone and extend its residence refining CO2ERR. Building on prior reactor designs, this work introduces the N‐reactor (the nozzle‐type reactor) with a ring electrode suited for gas–liquid mixing and reaction interplay. Through finite element simulations using the gas–liquid flow model, compared with the S‐reactors (the wide‐straight‐type reactor and the narrow‐straight‐type reactor), the N‐reactor's ring electrode shows a 12.99% CO2 concentration rise in the electrode zone and a 67.11% surge at the cathode exit. As a consequence, the total concentration of the product methanol is increased by 6.37%, with a maximum concentration increase of 26.96% and a concentration increase of 49.08% at the cathode outlet. These results validate the feasibility of optimizing the reaction from the perspective of gas–liquid mixing flow and provide novel methods and ideas for further optimization of CO2ERR, contributing to the practical application of the technology.

A Minimum Entropy Production Approach to Optimization of Tubular Chemical Reactors with Nature-Inspired Design

The problem of the shape optimization of tubular-type plug-flow chemical reactors equipped with a fluid flow-based cooling system is considered in this work. The hydraulic radius Rh(z) = 2A(z)/P(z) and an equivalent surface area-based radius Rs = P(z)/(2π) were computed from the cross-sectional area A(z) and perimeter P(z) measured along the nasal duct of Northern reindeer and used for shape optimization as nature-inspired design. The laminar flow in the cooling system was modeled using the Navier–Stokes equations for an incompressible liquid. In the central tube, a set of chemical reactions with temperature-dependent rates was considered. The temperature and flow velocity fields, pumping pressure, mass flow rate, and total heat flux Jth were obtained by numerical methods. Comparative analyses of the efficiency of different geometries were conducted on Pareto frontiers for hydraulic resistivity Zh, thermal resistivity Zth, thermal inlet length Lth, and entropy production Sirr as a sum of contributions from chemical reactions, thermal, and viscous dissipation. It was shown that the tube with Rs(z) as an interface between the reactor and cooler has the best Pareto efficiency using the (Zh,Zth,Lth) objective functions. Surprisingly, this design also exhibits the lowest Sirr and a more uniform distribution Sirr(z) (i.e., equipartition) among other designs. This geometry is suggested for densely packed tubular reactors.

Redesigning product line for integrated manufacturer-supplier ecosystem in a centralized supply chain: Case of an industrial consumer product

To produce quality products rapidly to derive competitive advantage, the original equipment manufacturer (OEM) must work closely with its suppliers in product redesign processes. Previous literature has emphasized that quality products can be produced effectively and will provide better customer service if product redesign is performed considering the broad OEM-supplier(s) ecosystem. To this end, we seek to develop a mathematical model focused on maximizing the objectives of the OEM-supplier(s) ecosystem considering a centralized supply chain in such a manner that operational-level financial objectives are mapped in terms of product line design decisions and attribute-level assignments. By including operational-level supply chain considerations such as service levels and consumers' preferences, we seek to capture key aspects related to customers’ requirements. This research links product-level technical decisions with enterprise-level business objectives, thus bridging engineering and enterprise-level financial nuances. The problem for the manufacturer pertains to the minimization of the manufacturing cost and maximization of the product premium. The problem for the suppliers pertains to maximization of salvage value, minimization of shortage cost, and maximization of sales revenue. These two problems are then sewed together, and a chemical reaction optimization (CRO) based solution methodology has been deployed. To solve and analyze the devised problem, a case study based on an industrial consumer product is conducted. We evolve a comprehensive mathematical framework that mimics the facets related to product line redesign encapsulating considerations pertaining to functional level design, supply chain dimensions, and manufacturer-supplier profitability. Several novel findings are reported based on the study. For instance, we demonstrate that product configurations for respective segments after the redesign process are contingent upon the service level. Further, we demonstrate that in the industrial consumer product market, higher profit for the OEM-supplier(s) ecosystem is achieved at a slightly lower service level as opposed to a higher theoretical service level. The study has important implications for design and product management.