Publication speed, impact, uncited share, self-citation, retractions and scale for Chemical Engineering Research and Design and Journal of Loss Prevention in the Process Industries, each metric with its unit and which direction is better.

Publication speed, impact, uncited share, self-citation, retractions and scale for Chemical & Engineering News and Industrial & Engineering Chemistry Process Design and Development, each metric with its unit and which direction is better.

China outsourced engineering and engineering-led project delivery for chemical and process-industry plants: FEED, detailed engineering, EPCM, engineering-led EPC/EPCC, procurement, project management, commissioning and process-technology/basic-engineering work for chemical, petrochemical, refining, gas-processing/LNG, polymer, fertilizer, industrial gas, hydrogen, ammonia, methanol, sustainable-fuels and adjacent process facilities. Excludes chemical producers, distributors, standalone catalyst/material sales, general civil/building engineering, O&M-only maintenance, offshore/subsea-only work, and standalone power/grid/renewables/mining/transport engineering.

CHEMCAD is a suite of software for the simulation of chemical processes and the design of equipment. Mathematica is an entirely different type of software, providing powerful computer algebra tools and mathematical functions for the theoretical or numerical solution of advanced mathematical problems. This data set provides instructions with an example for connecting CHEMCAD to Mathematica through Excel. The example is a simple well-mixed membrane calculation with a fully specified feed stream split by the membrane into retentate and permeate streams. This data set is similar to Reference 1 in that the same feed and membrane specifications are used. In Reference 1, we used the algebraic solution provided in Reference 2, which was arrived at by the authors after a series of algebraic manipulations. What makes this data set different is that we invoked the symbolic engine in Mathematica, specifying only the governing equations and letting Mathematica arrive at the solution. This makes the model far more flexible since additional components can easily be added by adding additional flux and species mole balance equations and letting Mathematica re-derive the solution. The results are interesting because a wide range of advanced design and simulation equations can be posed in Mathematica and then used directly in simulations called from and running live in CHEMCAD.

Files and instructions for connecting the software using Mathematica Link for Excel are included. The software prerequisites are working, licensed copies of Mathematica, CHEMCAD, and Mathematica Link for Excel. Users without licenses can request free trials from the vendors.

Synthetic Chemical Process Monitoring Time-Series Dataset

Overview

This dataset simulates a continuous chemical reactor system with realistic industrial sensor behavior, control dynamics, seasonal effects, multiple operating regimes, and gradual fault development.

The data is fully synthetic and designed to resemble real-world process historian data used in: - Industrial anomaly detection - Predictive maintenance - Fault diagnosis - Multivariate time-series forecasting - Robustness and domain-shift experiments

No real plant or proprietary data is used.

Key Characteristics

Operating Regimes

The dataset contains two distinct operating regimes, representing different production conditions.

Regime A — Baseline Operation

• Nominal temperature and pressure
• Efficient cooling
• Lower fault probability
• Represents stable long-term operation

Regime B — Stress Operation

• Higher temperature and pressure
• Reduced cooling efficiency
• Increased sensor noise
• Higher fault probability
• Represents harsh or high-throughput conditions

The column operating_regime identifies the regime (A or B).

Multi-Reactor Setup

Each operating regime includes multiple reactors operating in parallel. - Reactors follow the same process logic - Each reactor has small unit-specific differences - Enables: - Cross-asset generalization - Leave-one-reactor-out validation - Transfer learning experiments

The column reactor_id uniquely identifies each reactor.

Seasonal Ambient Effects

A slow seasonal ambient temperature effect is simulated using a sinusoidal pattern over time.

This ambient effect influences: - Cooling efficiency - Reactor temperature variability - Pressure stability

The column ambient_temp_effect represents this seasonal influence.

Fault Types

The dataset includes four realistic industrial fault modes:

Fault Behavior

• Faults develop gradually, not instantaneously
• Sensor drift and variance increase before failure
• Process efficiency degrades progressively
• Fault classes are imbalanced, reflecting real industrial data

Machine Learning Targets

Classification

• fault_type — multi-class fault diagnosis (0–4)

Regression

• efficiency_loss_pct — estimated production efficiency degradation

Forecasting / Survival Analysis

• time_to_fault_min — remaining time before fault onset

Column Descriptions (Data Dictionary)

Missing Data

To reflect real industrial conditions: - Random sensor dropouts are introduced - Approximately 6% missing values - Missingness is **not target...

The Github Repository, https://github.com/jodhernandezbe/TRI4PLADS/tree/v1.0.0,, is publicly available and referenced in supplementary information. This GitHub repository describes the computational framework overview, software requirements, model use, model output, and disclaimer. This repository presents a multi-scale framework that combines data engineering with process systems engineering (PSE) to enhance the precision of chemical flow analysis (CFA) at the end-of-life (EoL) stage. The focus is on chemicals used in plastic manufacturing, tracing their flows through the supply chain and EoL pathways. Additionally, this study examines potential discharges from material recovery facilities to publicly owned treatment works (POTW) facilities, recognizing their relevance to human and environmental health. Tracking these discharges is critical, as industrial EoL material transfers to POTWs can interfere with biological treatment processes, leading to unintended environmental chemical releases. By integrating data-driven methodologies with mechanistic modeling, this framework supports the identification, quantification, and regulatory assessment of chemical discharges, providing a science-based foundation for industrial and policy decision-making in sustainable material and water management. The attached file CoU - Metadata File.xlsx contains the datasets to build Figure 3 and describe a qualitative flow diagram of methyl methacrylate from manufacturing to potential consumer products generated from the Chemical Conditions of Use Locator methodology (https://doi.org/10.1111/jiec.13626). The attached file "MMA POTW Dataset.xlsx" contains the datasets needed to run the Chemical Tracker and Exposure Assessor in Publicly Owned Treatment Works Model (ChemTEAPOTW) as described in the Github Repository https://github.com/gruizmer/ChemTEAPOTW. The attached file "Plastic Data-Calculations-Assumptions.docx" contains all calculations and assumption to estimate the methyl methacrylate (MMA) releases from plastic recycling. Finally, users can generate Figures 4 and 5 after following the step-by-step process described in main Github repository for the MMA case study. This dataset is associated with the following publication: Hernandez-Betancur, J.D., J.D. Chea, D. Perez, and G.J. Ruiz-Mercado. Integrating data engineering and process systems engineering for end-of-life chemical flow analysis. COMPUTERS AND CHEMICAL ENGINEERING. Elsevier Science Ltd, New York, NY, USA, 204: 109414, (2026).

The Chemical Process Development Service Market was valued at USD 7.73 Billion in 2025 and is projected to grow to USD 12.5 Billion by 2035, at a CAGR of 4.9%. Chemical Process Development Service Market Overview: The Chemical Process Development Service Market Size was valued at 7.37 USD Billion in 2024. The Chemical Process Development Service Market is expected to grow from 7.73 USD Billion in 2025 to 12.5 USD Billion by 2035. The Chemical Process Development Service Market CAGR (growth rate) is expected to be around 4.9% during the forecast period (2025 - 2035). Key Chemical Process Development Service Market Trends Highlighted The Global Chemical Process Development Service Market is experiencing significant growth, driven primarily by the increasing demand for efficient and sustainable chemical production processes. One of the key market drivers is the rising need for innovation in the chemical industry, which encourages companies to seek out specialized services that enhance productivity and reduce environmental impact. Additionally, regulatory pressures to minimize carbon footprints and adhere to stricter safety standards are pushing firms towards adopting advanced process development services. There are numerous opportunities to be explored within this market, particularly in the realm of green chemistry and the development of sustainable alternatives to traditional chemical processes.Companies focusing on renewable feedstocks, waste reduction, and energy efficiency can capture substantial market shares by aligning with global sustainability initiatives. Emerging markets, particularly in Asia-Pacific and Latin America, are also ripe for expansion, as industries in these regions increasingly seek modernization and compliance with international standards. Trends in recent times show a noticeable shift towards digital transformation within chemical process development. The integration of artificial intelligence and machine learning into chemical engineering processes is enabling more efficient workflows and accelerated product development timelines.Moreover, collaboration between industry players and academic institutions is fostering innovation and pushing the boundaries of current chemical processes. Also, the use of simulation and modeling tools is gaining traction, which allows companies to optimize their processes with greater accuracy. Overall, the interplay of these trends is propelling the market towards more innovative, efficient, and sustainable practices in chemical process development on a global scale. Source: Primary Research, Secondary Research, WGR Database and Analyst Review Chemical Process Development Service Market Segment Insights: Chemical Process Development Service Market Regional Insights The Global Chemical Process Development Service Market is experiencing noteworthy growth across various regions, with the North America segment dominating the sector due to its substantial valuation. North America's market is expected to be valued at 2,250 USD Million in 2024 and projected to rise to 3,650 USD Million by 2035, showcasing significant growth driven by advancements in technology and an increasing focus on sustainable practices. Europe is also witnessing steady expansion, primarily fueled by stringent regulatory frameworks and the rising demand for innovation in chemical processes.The APAC region is showing strong growth, reflecting the rapid industrialization and increasing investments in Research and Development activities within emerging economies. Meanwhile, South America experiences moderate increase as local industries seek to enhance their competitiveness in the global market, while the Middle East and Africa (MEA) are on a gradual decline, facing challenges such as geopolitical tensions and fluctuating oil prices. Overall, the regional dynamics highlight varied trends, with North America firmly leading the market growth, driven by robust infrastructure and a strong emphasis on effective chemical process development. Source: Primary Research, Secondary Research, WGR Database and Analyst Review North America : The North American market is driven by a

Publication speed, impact, uncited share, self-citation, retractions and scale for Journal of Chemical Engineering of Japan and Industrial & Engineering Chemistry Process Design and Development, each metric with its unit and which direction is better.

Design principles an autothermal reactor with enhanced mass transfer for hydrogen production Junjie Chen Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com Chemical engineers are employed in the design and development of both processes and plant items. In each case, data and predictions often have to be obtained or confirmed with pilot experiments. Plant operation and control is increasingly the sphere of the chemical engineer rather than the chemist. Chemical engineering provides an ideal background for the economic evaluation of new projects and, in the plant construction sector, for marketing. The fundamental principles of chemical engineering underlie the operation of processes extending well beyond the boundaries of the chemical industry, and chemical engineers are employed in a range of operations outside traditional areas. Plastics, polymers, and synthetic fibres involve chemical-reaction engineering problems in their manufacture, with fluid flow and heat transfer considerations dominating their fabrication. The dyeing of a fibre is a mass-transfer problem. Pulp manufacture involve considerations of fluid flow and heat transfer. The nuclear industry makes similar demands on the chemical engineer, particularly for fuel manufacture and reprocessing. Chemical engineers are involved in many sectors of the metals processing industry, which extends from steel manufacture to separation of rare metals. Streamwise distance (millimeters), Reforming channel centerline temperature (degrees kelvin) 0 373.004 0.00025 373.059 0.0005 373.261 0.00075 373.771 0.001 374.741 0.00125 376.228 0.0015 378.219 0.00175 380.651 0.002 383.434 0.00225 386.47 0.0025 389.674 0.00275 393.013 0.003 396.47 0.00325 400.02 0.0035 403.634 0.00375 407.275 0.004 410.898 0.00425 414.453 0.0045 417.9 0.00475 421.231 0.005 424.454 0.00525 427.574 0.0055 430.596 0.00575 433.522 0.006 436.342 0.00625 439.041 0.0065 441.606 0.00675 444.041 0.007 446.359 0.00725 448.572 0.0075 450.693 0.00775 452.727 0.008 454.676 0.00825 456.532 0.0085 458.289 0.00875 459.952 0.009 461.531 0.00925 463.036 0.0095 464.477 0.00975 465.858 0.01 467.18 0.01025 468.441 0.0105 469.635 0.01075 470.767 0.011 471.843 0.01125 472.873 0.0115 473.859 0.01175 474.805 0.012 475.713 0.01225 476.579 0.0125 477.403 0.01275 478.186 0.013 478.933 0.01325 479.649 0.0135 480.337 0.01375 480.998 0.014 481.634 0.01425 482.243 0.0145 482.823 0.01475 483.376 0.015 483.906 0.01525 484.415 0.0155 484.905 0.01575 485.379 0.016 485.835 0.01625 486.273 0.0165 486.692 0.01675 487.093 0.017 487.477 0.01725 487.848 0.0175 488.206 0.01775 488.553 0.018 488.889 0.01825 489.212 0.0185 489.522 0.01875 489.82 0.019 490.106 0.01925 490.383 0.0195 490.651 0.01975 490.913 0.02 491.168 0.02025 491.413 0.0205 491.648 0.02075 491.875 0.021 492.093 0.02125 492.306 0.0215 492.513 0.02175 492.716 0.022 492.915 0.02225 493.106 0.0225 493.291 0.02275 493.469 0.023 493.642 0.02325 493.811 0.0235 493.977 0.02375 494.141 0.024 494.301 0.02425 494.456 0.0245 494.607 0.02475 494.753 0.025 494.895 0.02525 495.036 0.0255 495.174 0.02575 495.311 0.026 495.446 0.02625 495.578 0.0265 495.706 0.02675 495.831 0.027 495.953 0.02725 496.074 0.0275 496.195 0.02775 496.315 0.028 496.433 0.02825 496.549 0.0285 496.661 0.02875 496.772 0.029 496.881 0.02925 496.99 0.0295 497.092 0.02975 497.232 0.03 497.324 Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com, Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China

Global Chemical Engineering Simulation Software Market is segmented by Application (Process Simulation_ Product Design_ Chemical Processing_ Industrial Applications_ Research & Development), Type (Software_ Technology_ Engineering_ R&D_ Manufacturing), and Geography (North America, LATAM, West Europe, Central & Eastern Europe, Northern Europe, Southern Europe, East Asia, Southeast Asia, South Asia, Central Asia, Oceania, MEA)

The repository contains process data for Methanol to Olefin production modelled in Aspen Plus v12. Data is presented in the form of an Excel file, which includes details of the process, a process flow diagram, an Aspen simulation diagram, Aspen inputs and assumptions, simulation equipment details, and mass and energy balance information.

• Process name: MO1. Methanol to Olefin
• Process type: ACS-based
• Chemical type: Chemical building block
• Feedstock: Methanol
• Products: Ethylene, Propylene, Butene mixture, Benzene, etc.

Chemical Engineering Simulation Software Market size was valued at USD 1.46 Billion in 2024 and is projected to reach USD 3.16 Billion by 2032, growing at a CAGR of 3.10% from 2026 to 2032.Global Chemical Engineering Simulation Software Market OverviewThe global market is witnessing strong adoption as process industries prioritize cost optimization, operational reliability, and faster project execution. Simulation software provides value across key use cases such as:Process conceptualization & scale-up: evaluating flowsheet alternatives and reaction pathways before pilot/plant investmentsEnergy optimization: pinch analysis, heat integration, and utility reduction initiativesOperational troubleshooting: debottlenecking, constraint identification, and yield enhancementSafety & compliance: scenario analysis, relief system checks (where integrated), and operating envelope validationDigital twin enablement: connecting validated process models with real plant data for performance monitoring

Graph and download economic data for Producer Price Index by Industry: Chemical Manufacturing (PCU325325) from Dec 1984 to Aug 2026 about chemicals, manufacturing, PPI, inflation, industry, price index, indexes, price, and USA.

Unprocessed data for scientific article: Net-zero emissions chemical industry in a world of limited resources.

Authors of the article: Paolo Gabrielli, Lorenzo Rosa, Matteo Gazzani, Raoul Meys, André Bardow, Marco Mazzotti, Giovanni Sansavini

The article is published on One Earth and can be cited as: Gabrielli, P. et al. (2023) Net-zero emissions chemical industry in a world of limited resources, One Earth 6, https://doi.org/10.1016/j.oneear.2023.05.006

Data creator and curator: Paolo Gabrielli

The repository contains process data for CO2 based direct electrochemical reduction to ethylene production modelled in Aspen Plus v12. Data is presented in the form of an Excel file, which includes details of the process, a process flow diagram, an Aspen simulation diagram, Aspen inputs and assumptions, simulation equipment details, and mass and energy balance information.

• Process name: CO1. CO2 direct electrochemical reduction to ethylene
• Process type: ACS-based
• Chemical type: Chemical building block
• Feedstock: CO2
• Products: Ethylene and oxygen

View Peak Process Machine Engineering Chemical Industry And External Trade Anonymous Company import export trade data, including shipment records, HS codes, top buyers, suppliers, trade values, and global market insights.

LibertyCES Industrial Chemical Equipment Data

Real, open industrial chemical equipment engineering data published by LibertyCES (Liberty Chemical Equipment & Supply, Inc.) — a specifier and supplier of industrial pumps, valves, tanks, chemical feed systems, and containment equipment for municipal water/wastewater treatment, chemical processing, mining, and semiconductor manufacturing. Website: https://www.libertyces.com Live browsable version of this data:… See the full description on the dataset page: https://huggingface.co/datasets/benriggins/libertyces-industrial-chemical-equipment-data.

AI-Enhanced Chemical Process Simulation Market Outlook

According to our latest research, the AI-Enhanced Chemical Process Simulation market size reached USD 1.66 billion in 2025 globally, exhibiting robust growth driven by rapid digital transformation across the chemical and process industries. The market is expected to expand at a CAGR of 17.6% from 2026 to 2034, reaching a forecasted value of USD 7.17 billion by 2034. This impressive growth trajectory is fueled by increasing adoption of artificial intelligence for process optimization, safety enhancement, and operational efficiency in sectors such as oil & gas, chemicals, and pharmaceuticals. As per our latest research, the convergence of AI technologies with traditional process simulation tools is fundamentally reshaping the landscape of process engineering and plant operations. Advances in AI adoption across the chemicals sector are creating a strong foundation for sustained investment in simulation platforms through the forecast period.

One of the primary growth factors driving the AI-Enhanced Chemical Process Simulation market is the escalating demand for process optimization and efficiency improvements across the chemical manufacturing value chain. Industries are leveraging AI-driven simulation software to model complex chemical processes, enabling faster and more accurate predictions of process behaviors under varying conditions. This results in significant cost savings, reduced energy consumption, and minimized waste generation. Furthermore, AI-enhanced simulations facilitate real-time monitoring and predictive maintenance, which help in proactively addressing equipment failures and process deviations, thereby maximizing uptime and productivity. The integration of machine learning algorithms into simulation platforms empowers engineers to derive actionable insights from vast datasets, accelerating product development cycles and supporting innovation in process design. The growing maturity of chemical process optimization using AI is enabling vendors to deliver measurably higher ROI to industrial clients in 2025 and beyond.

Another significant driver for market expansion is the growing emphasis on safety and regulatory compliance within the chemical process industries. The ability of AI-enhanced simulation tools to conduct comprehensive safety analyses and risk assessments has become indispensable in meeting stringent government regulations and industry standards. These advanced solutions enable organizations to simulate hazardous scenarios, evaluate mitigation strategies, and optimize emergency response protocols without exposing personnel or assets to actual risk. Moreover, AI-powered simulation platforms can continuously learn from historical incident data, improving the accuracy of hazard identification and prevention measures over time. This not only ensures regulatory compliance but also strengthens the overall safety culture within organizations, making AI-enhanced simulation an essential investment for risk-averse sectors in 2025.

The rapid digitalization of industries and the proliferation of cloud computing are further accelerating the adoption of AI-enhanced chemical process simulation solutions. Cloud-based deployment models offer unparalleled scalability, flexibility, and cost efficiency, allowing organizations to access advanced simulation capabilities without the burden of heavy upfront investments in IT infrastructure. This democratization of technology is particularly beneficial for small and medium-sized enterprises (SMEs), enabling them to compete on a level playing field with larger players. Additionally, the integration of AI with cloud platforms facilitates seamless collaboration among geographically dispersed teams, fosters innovation through shared data and models, and supports the implementation of Industry 4.0 initiatives across the process industries throughout the 2026-2034 forecast window.

From a regional persp

The repository contains process data for Aromatics production modelled in Aspen Plus v12. Data is presented in the form of an Excel file, which includes details of the process, a process flow diagram, an Aspen simulation diagram, Aspen inputs and assumptions, simulation equipment details, and mass and energy balance information.

• Process name: A1. Aromatics
• Process type: Fossil-based
• Chemical type: Chemical building block
• Feedstock: Reformate, Pygas and C7+
• Products: Benzene, o-xylene and p-xylene

Publication speed, impact, uncited share, self-citation, retractions and scale for The Journal of the Society of Chemical Industry Japan and Industrial & Engineering Chemistry Process Design and Development, each metric with its unit and which direction is better.