Efficient resource utilization, risk mitigation, and early anomaly detection are essential for the smooth operation of process industries such as chemicals, pharmaceuticals, oil and gas, and food processing. The integration of Artificial Intelligence (AI) and remote sensing technologies offers new opportunities to monitor assets, identify inefficiencies, and optimize decision-making processes in real-time. By leveraging machine learning (ML) algorithms and high-resolution imaging data, industries can detect deviations, classify materials, and forecast system failures more accurately and cost-effectively. This section briefly explores three key areas—classification, early detection, and decision-making—where AI-driven tools are being adapted to manage resources more intelligently in industrial settings.

In process industries, accurate identification of raw materials, intermediate products, or waste streams is vital for efficient resource management and quality control. Traditionally, laboratory-based spectral analysis has been used to differentiate between materials, but real-time implementation in industrial settings has been limited due to the lack of high-resolution imaging, robust machine learning (ML) algorithms, and reliable sensor data.

Recent advancements in data fusion techniques—especially combining hyperspectral imaging with LiDAR—have shown promise for on-site material classification. Convolutional Neural Networks (CNNs) can analyze hyperspectral signatures and incorporate spatial characteristics using LiDAR inputs [25]. For example, a custom CNN model trained on hyperspectral and RGB images, along with LiDAR data collected using aerial platforms, demonstrated significant accuracy in classifying different tree species based on field-verified data [26]. When applied to process industries, similar methodologies could be used to classify different materials on conveyor belts, detect contamination in pipelines, or identify corrosion in storage tanks.

Notably, the hyperspectral CNN model in that forestry study outperformed the RGB-based model by a margin of 23%, with overall classification accuracy reaching 87% [27]. This suggests that integrating high-resolution multisensor data with ML algorithms could form a repeatable and scalable approach for material identification in industrial environments.

Timely identification of equipment faults, chemical leaks, or microbial contamination is crucial in process industries to prevent costly downtime and ensure product integrity. In high-value sectors such as food processing or pharmaceuticals, early detection can prevent widespread product recalls and protect consumer safety.

Drawing parallels from agriculture, where hyperspectral drone imaging has been used to detect diseases like citrus greening with up to 99.7% accuracy, similar ML-based monitoring systems can be adopted in industrial settings [28]. These systems rely on multi-feature fusion—combining vegetation indices (analogous to spectral or thermal indicators in industrial equipment) and physical attributes (e.g., heat signatures, surface area changes)—to distinguish between healthy and compromised components or batches.

Implementing such AI-driven inspection frameworks using drones, robots, or mounted sensors can significantly reduce inspection time and costs [29]. Moreover, integrating them into predictive maintenance systems could allow facilities to quarantine affected sections, reduce risks of large-scale failures, and optimize resource use by targeting interventions more precisely.

Strategic investment and resource allocation are fundamental to efficient operations in process industries, especially when balancing safety, environmental compliance, and production goals. Artificial Intelligence (AI) can support these decisions by automating risk assessments and optimizing maintenance schedules [30].

For instance, a decision-tree model applied to urban forestry successfully assessed the collapse risk of trees using just 14 out of 36 initially measured variables [31]. In process industries, similar methodologies could assess the risk of equipment failure or production bottlenecks using a reduced set of critical indicators—such as vibration, temperature fluctuations, or pressure variances—collected through sensors and analyzed using decision-tree or other classification algorithms [33].

A case study from Brazil used mobile data collection tools to evaluate tree risks and achieved a 73% accuracy rate in prioritizing interventions [30]. Translating this approach, plant managers can develop AI-based protocols to rank and address the most pressing operational hazards. Though current models may struggle with edge cases, ongoing refinement using larger and more diverse datasets will improve accuracy and applicability over time [33].

In the field of remote sensing and geospatial data analysis, the efficient management of massive datasets is crucial. To address the growing demand for large-scale data processing, several specialized platforms have been developed. These tools not only enable researchers and professionals to analyze spatial information effectively but also support a wide array of applications ranging from environmental monitoring to disaster management. This section explores five prominent geospatial software tools—Google Earth Engine, ENVI, ERDAS IMAGINE, Global Mapper, and others—highlighting their features, strengths, and relevance in managing complex geospatial resources.

Launched in 2010, Google Earth Engine (GEE) is a cloud-based geospatial processing platform built upon Google's computational infrastructure. It provides researchers with free access to an extensive data catalog—including Landsat, Sentinel, MODIS, and climate datasets—and supports JavaScript, Python, and R for algorithm development [34]. Tailored for educational and research purposes, GEE provides free access to a powerful suite of tools designed for the large-scale analysis of spatial data. Its core mission is to support scientific advancement by facilitating unrestricted access to a vast array of datasets and computational tools, particularly for addressing global-scale challenges in environmental monitoring and big data analytics.

GEE hosts an extensive data catalog, featuring petabytes of satellite imagery and geophysical datasets, including those from Landsat, MODIS, and Sentinel missions. Users can interact with the platform through a JavaScript-based code editor or utilize the Python and R APIs for advanced scripting. For those unfamiliar with programming, GEE offers a simplified interface known as “Explorer” to perform essential operations. Additionally, GEE allows for both importing local data and exporting results for further analysis in desktop GIS tools such as QGIS and ArcGIS Pro [35].

Widely used in remote sensing and geoscience, GEE supports a diverse range of applications—from crop monitoring [36] and vegetation tracking [37] to land cover classification and forest fire analysis [38]. It has played a key role in academic and practical research, especially when supported by bibliometric studies that trace evolving trends and uncover knowledge gaps [39]. The integration of AI technologies—such as machine learning (ML), deep learning (DL), and computer vision—has further expanded its capabilities, particularly in high-impact domains like natural disaster assessment and healthcare analytics. The emerging field of GeoAI, which combines geospatial science with AI and high-performance computing, demonstrates how GEE is central to extracting valuable insights from geospatial big data.

Global Mapper is another user-friendly GIS software package, released in 2001, and recognized for its versatility in handling a wide range of spatial data. Supporting over 300 file formats, it offers robust functionality for managing vector, raster, elevation, and point cloud data. Its real-time reprojection capabilities and integration with online data services—such as aerial and satellite imagery—make it a highly adaptable tool for geospatial analysis [40].

The software provides a powerful set of tools for vector digitization, spatial operations (like intersection and union), and attribute editing. Global Mapper also features scripting and batch processing, allowing users to automate routine GIS tasks and streamline workflows. Its “Pixels-to-Points” tool is particularly useful for UAV users, enabling the conversion of aerial imagery into high-resolution 3D models and dense point clouds using photogrammetry techniques [41].

Introduced in 1994, The Environment for Visualizing Images (ENVI) is a remote sensing software suite designed for users at all skill levels, offering a blend of usability and advanced analytical capabilities. Its interface simplifies the image processing workflow by organizing tasks into guided steps, covering operations such as calibration, atmospheric correction, orthorectification, and image registration [42]. A typical spectral workflow in ENVI includes data preparation, creating spectral libraries, target detection, mapping unique materials, and visualization

ENVI is compatible with a wide variety of data formats, including HDF, CDF, GeoTIFF, and NITF, and supports multiple sensor types—radar, LiDAR, SAR, multispectral, hyperspectral, thermal, and more. The platform integrates data from modern satellite and airborne systems such as Sentinel, AVIRIS, WorldView, and Planet Dove. Its spectral analysis capabilities allow for accurate material detection, vegetation monitoring, and environmental assessment.

Recent research underscores ENVI's strength in hyperspectral image processing [43]. Studies have used the software to detect bruises on fruits, monitor quality in agricultural produce, and identify contaminants. ENVI's flexible workflows support tasks such as anomaly detection, feature extraction, and terrain modeling, making it a versatile solution in both scientific and applied geospatial contexts.

Another tool: ERDAS IMAGINE is another comprehensive software suite focused on remote sensing data analysis and image classification. Its intuitive interface supports a broad spectrum of geospatial workflows, such as orthorectification, mosaicking, terrain categorization, and LiDAR editing. Designed for both beginners and experts, it emphasizes ease of use while offering powerful backend algorithms for efficient processing [44].

The software includes tools for supervised and unsupervised image classification, terrain modeling, and spatial analysis. ERDAS IMAGINE also provides scripting capabilities through its Macro Language (EML) and supports AI-based workflows with tools for training and deploying ML and DL models. These features are especially useful for feature extraction and land cover classification [45].

The “Advantage” edition of the software builds upon the core functionalities by offering more advanced mapping tools, point cloud editing, and radar analysis. It has been effectively employed in environmental monitoring, natural resource management, and defense applications, and its image enhancement capabilities—such as histogram equalization and Gaussian filtering—help improve image clarity and detail [45]

Supply chains are intricate systems made up of numerous interdependent business relationships focused on the creation and delivery of value. Traditional supply chains often lack transparency and trust. The exchange of information, especially between geographically dispersed partners, typically demands significant resources, intermediaries, and time. Many transactions are still conducted on paper, making the system vulnerable to fraud, asymmetric information, and operational inefficiencies. Another pressing challenge is coordinating effectively among supply chain stakeholders. Even though tools like enterprise resource planning (ERP) systems and digital tracking technologies are employed, issues related to complexity and errors persist [68].

In light of these challenges, blockchain has emerged as a particularly promising tool for transforming supply chain operations. By enabling complete auditability across the supply chain and reducing reliance on intermediaries, blockchain simplifies the process of tracking goods and transactions [67]. Through its capacity to consolidate data from various sources, blockchain fosters better communication among companies, encouraging the adoption of standardized protocols that facilitate strategic and mutually beneficial partnerships. As a result, blockchain significantly enhances supply chain mapping, auditing, and certification, helping companies build greater trust with end consumers [62].

Blockchain supports supply chain management through three key functionalities:

1. Transparency – The decentralized nature of blockchain ensures that the distributed ledger is visible to all participants.

2. Traceability – All actions and transactions are recorded in an immutable format, making it easy to track the origin and movement of goods.

3. Authentication – All network participants participate in verifying and storing transaction data (Wang et al., 2020).

In some systems, supply chain transactions are logged automatically using smart contracts or Internet of Things (IoT) sensors. Smart contracts offer a way to digitally enforce agreements and procedures, blending user interfaces with secure protocols to facilitate automated, trustworthy operations. In recent years, major global corporations such as Carrefour, Walmart, Nestlé, and Alibaba have started incorporating blockchain into their supply chain processes [66].

Beyond supply chains, blockchain also offers valuable benefits for the Circular Economy (CE) and waste management sectors [69]. Blockchain enhances CE practices by enabling seamless information flow along supply chains, which is essential for materials and by-products exchange. It secures data management, ensures shared access to key information among stakeholders, and strengthens collaborative efforts. Within waste management, programs aimed at waste exchange often suffer from fraud, data inaccuracy, and inefficient manual handling. Blockchain can address these issues by ensuring the reliability and availability of data.

Similarly, the Industrial Symbiosis (IS) framework—which focuses on collaboration among firms to share resources, such as energy, materials, and waste—stands to benefit significantly from blockchain integration. Both IS and blockchain operate as decentralized ecosystems: blockchain is a peer-to-peer network where information is securely stored and shared, while IS is a platform for inter-firm collaboration, often aligned with CE principles. Each involves multiple actors working together to create synergies beyond what individual firms can achieve alone – see Figure 3 where the concept of resource exchange can be managed by a smart transaction framework, namely either Pinch Analysis or some allocation methodology, through an internal cryptocurrency between the entities in the region/sites [49].

Figure 3.

Concept of using blockchain in managing resource exchange between sites, adapted from [49]

However, both IS and blockchain still face several shared barriers, including a lack of robust information systems, technical difficulties in data gathering, and challenges in encouraging collaboration, trust, and willingness among participants [70]. Sensitive data—such as waste quantities, production volumes, and pricing—often remain undisclosed due to competitive concerns, further complicating IS data collection efforts [69].

Integrating blockchain into IS platforms could mitigate many of these issues. With its secure, transparent, and immutable structure, blockchain can enhance data infrastructure, promote trust, and streamline collaboration. Despite growing academic interest in the potential of blockchain within CE and IS contexts, there remains a notable gap in research specifically linking blockchain to IS frameworks.

Originally developed for thermodynamic systems, Pinch Analysis has been effectively adapted for various supply chain applications, including optimization, performance evaluation, and environmental impact analysis. The technique was initially used to improve energy efficiency by optimizing the heat exchange between industrial processes requiring heating and cooling. In this context, Pinch Analysis identifies opportunities to recover and reuse heat across processes [71].

In supply chain management, a similar principle is employed by plotting supply and demand curves separately and analyzing their interactions over time. These curves help to visualize inventory levels at different points in a planning horizon, making it easier to detect imbalances such as surpluses or shortages. This graphical approach provides decision-makers with insights into energy usage and stock availability, allowing timely adjustments [72].

The first known application of Pinch Analysis to supply chain planning was introduced in 2002 [73]. This approach used a combinatorial graph with product quantity on the horizontal axis and time on the vertical, to identify the minimum production levels needed to meet demand while revealing inventory fluctuations. The "Pinch Point" marks the point of zero inventory, guiding adjustments in production, storage, and logistics to optimize profitability while satisfying demand requirements. Later, Singhvi et al. [74] validated this method using case studies and compared it with solutions generated by the GAMS software. The pinch-based approach significantly reduced computational time—up to six times faster than directly solving the mathematical model.

Further innovations extended Pinch Analysis to cost minimization in resource allocation problems [75]. By developing prioritization criteria and applying the analysis, researchers proposed efficient methods for internal and cross-regional resource management, balancing the use of dedicated and shared assets.

The methodology has also been adapted to address environmental concerns, such as reducing carbon emissions. For example, Carbon Pinch Analysis provides a visual tool for understanding how changes in energy sources affect emissions, helping regions to meet carbon reduction goals [76]. Li et al. [77] applied this approach to plan regional electricity and biomass supply chains under carbon constraints. Adjustments in biomass supply were mapped against energy demands and emission targets to identify the minimal requirement for external electricity sources.

Hwangbo et al. [78] introduced a hybrid Pinch Analysis combining carbon dioxide and hydrogen metrics, allowing comparative assessments of environmental impacts in hydrogen energy systems. Their graphical analysis displayed CO₂-equivalent emissions for different hydrogen production processes. Similarly, another study [79] used Pinch Analysis to assess greenhouse gas emissions across different bioenergy supply chain configurations.

As Pinch Analysis matured, it was further extended to handle complex, real-world challenges like uncertainty and multiple objectives. For instance, Priya and Bandyopadhyay [80] developed a multi-objective version of the analysis that balanced cost and resource quality in supply planning. Bandyopadhyay [81] explored uncertain environments by introducing interval-based representations of unknown parameters and applying the approach to develop feasible planning solutions without precise input data.

Jalanko and Mahalec [82] applied a supply-demand pinch method for optimizing gasoline blending over multiple time periods under component quality uncertainty. Their evaluation of this technique against comprehensive models demonstrated its potential for producing quality solutions efficiently.

Overall, the key strength of Pinch Analysis lies in its ability to deliver intuitive and visual solutions – see Figure 4. It facilitates energy savings, cost reductions, and more efficient supply chain operations by streamlining heat integration and resource allocation. Through straightforward calculations, planners can quickly outline preliminary supply-demand scenarios. Additionally, adjusting supply curves allows for effective comparison of different planning scenarios. Importantly, the method offers a robust starting point for mathematical programming, improving convergence speed and solution quality. Despite its growing versatility—spanning uncertain and multi-objective optimization—further research is needed to refine the method’s reliability and broaden its applicability across diverse planning contexts.

Figure 4.

Classic Pinch Analysis diagram to visualize resources allocation

Recent studies have expanded the application of the Pinch-based approach to various forms of material integration. Chin et al. (2020a) investigated the foundational principles in resource conservation networks and developed a Pinch-based targeting methodology for networks involving multiple types of contaminants. Yanwarizal et al. (2020) proposed a framework that separates limiting Composite Curves at the stream level instead of aggregating them, enabling more precise targeting and design. Oladosu et al. (2020) introduced an algebraic method that addresses different stream splitting configurations within this problem domain. For comprehensive reviews on advanced Pinch-based methods, readers may refer to Klemeš et al. (2018) on mass-based and heat-based integration applications.