Jeffrey Dean Kelly

• B.A.Sc., M.Eng.
• CEO at Industrial Algorithms Limited

Modeling and optimization of large-scale refinery scheduling problems is challenging because of their complexity and size. Herein, we propose a mathematical model to represent such problems more accurately and realistically, and a state-of-the-art optimization framework for its solution. The framework leverages the use of mathematical optimization...

A hybrid model predictive control formulation with continuous and discrete variables that models multiple crushed-ore stockpiles per conveyor-belt is proposed for better conveying and stockpiling performance. The aim is to minimize the inventory level or holdup squared deviations from targets by varying the fill-time, idle-time, or run-length of th...

Data management systems are increasingly used in industrial processes. However, data collected as part of industrial process operations, such as sensor or measurement instruments data, contain various sources of errors that can hamper process analysis and decision making. The authors propose an operating-regime-based data processing framework for i...

Simultaneous blending and scheduling optimisation represents a mixed-integer nonlinear programming (MINLP) problem, whereby the binary variable relaxation that forms a nonlinear programming (NLP) in the first stage of a full-space algorithm (without dropping any decision variable) may lead to convergence issues. Furthermore, there is no guarantee t...

As an application of advanced analytics (AA) in supply chains (SCs), to model supply chain resilience (SCR) of transactions, logistics, operations, etc., of such complex representation of networks, we propose a supervised machine learning approach as a predictive analytics decision regression modelling framework that uses a coefficient setup MIQP (...

The Industrial Modeling and Programming Language (IMPL) is a sophisticated computational system for tackling large-scale and complex-scope data analytics and decision-making problems in the engineering and operations research fields. Although the software provides both scalar-based (e.g., MATLAB) and set-based modeling (GAMS, AIMMS, AMPL, MOSEL, OP...

Further to basic process control strategies, advanced process control (APC) aims to improve the performance of the control by actuating towards input moves against effects of process disturbances. Typically addressing economic-based goals as objectives, APC includes a wide range of techniques embedded in process control systems generally found in c...

Surrogate modeling has been increasingly used to predict the behavior of a given system as an alternative to complex formulations that often lead to time consuming solutions and convergence issues. Surrogates are addressed herein to replace complex formulations for reactor systems within optimization problems. An adaptive sampling algorithm explore...

As supply chains evolve from local trading entities to global physical and virtual markets, today’s organizations are privileged with enhanced access to unprecedented opportunities in volume, variety, deliver time, transportation mode, of resources and goods. However, given today’s level of interdependence of these entities that permits reduced mis...

In the wake of the COVID-19 pandemic, hospitals worldwide have been overwhelmed and deprived of valuable resources such as bed capacities, medical equipment, personal protection equipment (PPE) stocks, and personnel. These factors imposed unforeseen challenges in the healthcare treatment systems. Mitigating inefficiencies by learning from COVID-19...

Scheduling decision-making is often calculated and implemented using unreliable or inaccurate data from process networks, therefore infeasibilities and inconsistencies in the production are expected. For improved operations, it is fundamental to minimize plant-model mismatches, in which the current state of the system is continuously updated. The m...

As economies and industries evolve from local trading entities to global physical and virtual markets, today's organizations are privileged with enhanced access to unprecedented opportunities and resources. Simultaneously, such organizations are continuously challenged by increased disruptions and black swans. Over the past decades, global economie...

Many industrial engineering problems involve complex formulations and are assisted by simulation tools. Although these tools provide highly accurate solutions, they may not be suitable for large scale problems and for optimization applications. Looking for alternatives to complex formulations that often lead to convergence issues and to time consum...

Academia is an arena where practitioners from industry are integrated to theoreticians. Such alliance has been intensified by the industry 4.0 (I4) age from which these counterparts are seeking to merge efforts towards society 5.0 (S5), enabling next generations to easily accept novelties and changes in well-established operations, process-of-work,...

With the expansion of the global population coupled with the improving quality of life in developing countries, an increasing demand of food for humans and feed for livestock are expected. Therefore, global pressures and food security issues encourage research, development and deployment of alternatives to improve the performance of the production...

The Industrial Modeling and Programming Language (IMPL) software designed for the process system engineering (PSE) and operations research (OR) communities is architected to be a structural unit-operation-port-state superstructure (UOPSS) and a semantic quantity-logic-quality phenomena (QLQP) modeling language embedded into a computer programming l...

For high-performance operations in crude oil refinery processing, it is important to properly determine yields and properties of output streams from distillation units. To address such complex representation, we propose a cutpoint temperature modeling framework using a coefficient setup MIQP (mixed-integer quadratic programming) technique to determ...

Standard benchmarks are important repositories to establish comparisons between competing model and control methods, especially when a new method is proposed. This paper presents details of an Arduino micro-controller temperature control lab as a benchmark for modeling and control methods. As opposed to simulation studies, a physical benchmark cons...

IMPL is both a structure- and semantic-based machine-coded proprietary software language (closed-source) built upon the computer programming language Fortran to model and solve large-scale discrete, nonlinear and dynamic (DND) optimization and estimation problems found in the batch and continuous process industries such as oil and gas, petrochemica...

The solution of today's complex decision-making for smart manufacturing are dependent on the ability to: a) realistically model the manufacturing system, b) easily and timely integrate valid and consistent plant data, c) solve the problem efficiently with reasonable computational efforts, and d) incorporate feedback to continuously improve the deci...

High-quality process system engineering (PSE) solutions for production scheduling evolve to a wider scope and scale when moving from simulation- to optimization-based approaches, generally by using mixed-integer programming (MIP) in discrete-time formulations. To reach as we state in this paper as the spotting level of service, into the scheduling...

We describe the application of an online dynamic and discrete scheduling optimization, also known as real-time hybrid model predictive control, applied to a shuttle-conveyor / tripper car intermittently delivering crushed-ore containing copper, iron, etc., to several stockpiles. Each stockpile continuously feeds an apron feeder located in a tunnel...

In manufacturing with transformation of natural resources into products, different quality raw materials varying in composition are segregated, stocked and blended in order to prepare the bulk feed quality of the plant. In such blend scheduling problem, the topology of the storage and blend operations as well as the process design network can affec...

Considering the nascent application of advanced technologies in industry within the context of the new industrial revolution, we propose a methodology for identification and design (ID) of smart operations in manufacturing. The ID study considers the elements of the Industry 4.0 (I4) such as autonomous robots, advanced analytics, systems integratio...

We present an initiative for education in Process System Engineering (PSE) covering industrial applications in both prescriptive and predictive analytics. Prescriptive analytics or decision-automation is the science of automating the decision-making of any physical system with respect to its design, planning, scheduling, control and operation using...

The scheduling operations in crude-oil refinery industries are commonly based on simulation of discrete production scenarios for selection, sequence or setups of tanks and unit-operations considering a complex network of continuous-processes within a time-horizon of a week. Although series of works in academia consider continuous-time modeling for...

Advances in modeling and solving capabilities in the crude-oil refinery scheduling has recently addressed its optimization more accurately by considering wider scope, scale and complexities of the refining process network. In this work, we present examples of these enhancements such as a) a decomposition strategy to enable the optimization of large...

We develop a linear programming (LP) approach for nonlinear (NLP) blending of streams to approximate non-convex quality constraints by considering property variables as constants, parameters or coefficients of qualities that we call factors. In a blend-shop, these intensive properties of streams can be extended by multiplying the material flow carr...

Understanding the holistic relationship between refinery production scheduling (RPS) and the cyber-physical production environment with smart scheduling is a new question posed in the study of process systems engineering. Here, we discuss state-of-the-art RSPs in the crude-oil refining field and present examples that illustrate how smart scheduling...

As part of the freeware, open-use and community-based movement on education in the process system engineering discipline, we present the OpenIMPL initiative that is a forum to exchange ideas, learnings, know-how, experiences and data using the free training license of IMPL (Industrial Modeling and Programming Language) for open-use. IMPL is both a...

At the edge of the manufacturing of crude-oil distillates into refined final products, the production scheduling and distribution gap can be reduced by optimizing production rundown switches of dispositions of distillates in a mixed-integer linear model (MILP) considering discrete time-steps of days, shifts or hours for a delivery horizon of weeks...

Integrated scheduling optimization comprising the battery limits of crude-oil refineries is a challenging problem to be solved as it includes decisions concerning the quantity and quality of the crude-oil feedstocks and final products (such as fuels and petrochemicals) as well as production, processing or make-side of the refinery flow network. So...

A novel approach to scheduling the startup of oil and gas wells in multiple fields over a decade-plus discrete-time horizon is presented. The major innovation of our formulation is to treat each well or well type as a batch-process with time-varying yields or production rates that follow the declining, decaying or diminishing curve profile. Side or...

We propose a quantitative analysis of an enterprise-wide optimization for operations of crude-oil refineries considering the integration of planning and scheduling to close the decision-making gap between the procurement of raw materials or feedstocks and the operations of the production scheduling. From a month to an hour, re-planning and re-sched...

The focus of this paper is to detail the quantity and quality modeling aspects of production flowsheets found in all process industries. Production flowsheets are typically at a higher-level than process flowsheets given that in many cases more direct business or economic related decisions are being made such as maximizing profit and performance fo...

We propose a mixed-integer linear (MILP) model for the design of assignments of various raw materials with different qualities when moving them from external supply sources to shared storages. This is especially important in process industries with limited storage and quality blend programs optimizing a plant feed diet for ongoing operations involv...

We propose a discrete-time formulation for optimization of scheduling in crude-oil refineries considering both the logistics details practiced in industry and the process feed diet and quality calculations. The quantity-logic-quality phenomena (QLQP) involving a non-convex mixed-integer nonlinear (MINLP) problem is decomposed considering first the...

A mixed-integer linear programming (MILP) model is developed to determine optimal decisions concerning resource selection and sequencing operations over time in a water treatment facility (Fig. 1). Processing and cleaning timetables for a network of ion exchange resin beds are determined obeying specialized operational business rules. Measurement o...

]We present several applications and opportunities of smart process operations in fuels industries. The applications include (i) crude to tank assignment to minimize crude-oil quality variation, (ii) integration of distillation towers' cutpoint temperature optimization in blend-shops and (iii) hybrid real-time optimization (first-principles and dat...

In oil refinery manufacturing, final products such as fuels, lubricants and petrochemicals are produced from crude-oil in process units considering their operations in coordination with tanks, pipelines, blenders, etc. In this process, the full range of hydrocarbon components (crude-oil) is transformed (separated, reacted, blended) into smaller boi...

We propose a mixed-integer nonlinear optimization for process design synthesis of oil-refinery units that includes crude-oil mixing, unit processing and product blending. The quantity-logic-quality phenomena involving a non-convex mixed-integer nonlinear problem is decomposed into a two-stage stochastic programming model with complete recourse cons...

Due to quantity times quality nonlinear terms inherent in the oil-refining industry, performing industrial-sized capital investment planning (CIP) in this field is traditionally done using linear (LP) or nonlinear (NLP) models whereby a gamut of scenarios are generated and manually searched to make expand and/or install decisions. Though mixed-inte...

Oil-refining involves a series of complex manufacturing processes in which final products such as fuels, lubricants and petrochemical feedstocks are produced from crude-oil feedstocks by separation and conversion unit-operations in coordination with tanks, blenders and transportation vessels. To manage the processing of the hydrocarbon streams, wel...

The oil industry in Brazil has accounted for US$ 300 billion in investments over the last 10 years and further expansions are planned in order to supply the needs of the future fuel market in terms of both quantity and quality. This work analyzes the Brazilian fuel production and market scenarios considering the country’s planned investments to pre...

A novel technique using monotonic interpolation to blend and cut distillation temperatures and evaporations for petroleum fuels in an optimization environment is proposed. Blending distillation temperatures are well-known in simulations whereby cumulative evaporations at specific temperatures are mixed together; these data points are used in piece-...

Nonlinear planning and scheduling models for crude-oil atmospheric and vacuum distillation units are essential to manage increased complexities and narrow margins present in the petroleum industry. Traditionally, conventional swing-cut modeling is based on fixed yields with fixed properties for the hypothetical cuts that swing between adjacent ligh...

Current gasoline blend scheduling practice is to optimize blend plans via fixed duration (e.g., days) multiperiod NLP or MINLP models and schedule blends via interactive simulation. Solutions of multiperiod models typically have different blend recipes for each time period. We introduce inventory pinch points and use them to construct an algorithm...

Systems and methods that include selecting a physical model and a procedural model associated with an industrial plant, projecting a cross-product of the physical model and the procedural model into a unit-operation association set using a projection matrix, the unit-operation association set associating units representing equipment in the industri...

Detecting windows or intervals of when a continuous process is operating in a state of steadiness is useful especially when steady-state models are being used to optimize the process or plant on-line or in real-time. The term steady-state implies that the process is operating around some stable point or within some stationary region where it must b...

The purpose of this article is to express a novel stewardship procedure to aid in the detection and identification of production execution and process instrumentation problems. The technique bridges the gap between well-known data reconciliation and production scheduling by formulating both quantity and timing constraints into two reconciliation pr...

In this chapter the mathematical modeling of several types of inventories are detailed. The inventory types are classified In this chapter the mathematical modeling of several types of inventories are detailed. The inventory types are classified as batch processes, pools, pipe lines, pile lines and parcels. The key construct for all inventory model...

This paper presents a new technique for decomposing and rationalizing large decision-making problems into a common and consistent framework. We call this the hierarchical decomposition heuristic (HDH) which focuses on obtaining “globally feasible” solutions to the overall problem, i.e., solutions which are feasible for all decision-making elements...

New mathematical formulations of observability, redundancy, and an improved formulation for precision are provided which can be explicitly and analytically solved using mixed integer linear programming (MILP). By using the Schur complement found at the heart of both Gaussian elimination and Cholesky factorization for direct block matrix reduction a...

This paper presents a new mixed-integer linear program (MILP) formulation for modeling sequence-dependent switchovers for uniform discrete-time scheduling problems. The new formulation provides solutions faster than the formulation found in the paper by Kondili et al. (Comput. Chem. Eng. 1993, 17, 211) and scales more efficiently. The key to this f...

This paper presents a mixed-integer linear programming (MILP) model for the scheduling of a multistage process for the production of aluminum casts of different alloys, using parallel furnaces and casters. In contrast to the common approach in multistage models of considering a fixed set of orders being processed sequentially in the stages, the mod...

The focus of this article is to describe a relatively straightforward technique to improve the accuracy of predicting or tracing the internal qualities inside a production network when all of the stream qualities cannot be explicitly measured. The qualities usually represent intensive variables such as compositions, properties and conditions. Compo...

It is well-known that production scheduling can be largely categorized into open and closed-shops (Graves (1981)). Open-shops are job-shops, flow-shops, machine-shops and project-shops and deal exclusively with the assignment, sequencing and timing decisions (Pinedo (1995)). Closed-shops on the other hand also deal with the same three decision-grou...

Scheduling of cast house operations is difficult especially for multi-product casting operations. The cast house operations are generally batch oriented where as the pot line operations continuously produce hot metal which must be processed. This results in difficulties coordinating the pot line operations with the cast house operations, the net ef...

The focus of this short note is to highlight several techniques to solve industrial nonlinear data reconciliation problems. The main areas of discussion are starting value generation, row and column scaling, regularization of the kernel matrix, using different and independent unconstrained solving methods such as ridge regression, matrix projection...

Decomposing large problems into several smaller subproblems is well known in any problem solving endeavor and forms the basis for our flowsheet decomposition heuristic (FDH) described in this short note. It can be used as an effective strategy to decrease the time necessary to find good integer-feasible solutions when solving closed-shop scheduling...

Applications of nonlinear optimization problems with many degrees of freedom have become more common in the process industries, especially in the area of process operations. However, most widely used nonlinear programming (NLP) solvers are designed for the efficient solution of problems with few degrees of freedom. Here we consider a new NLP algori...

This short note describes the relevant details of formulating and implementing general bilinear quantity–quality balances found in industrial processes when data reconciliation is applied. The modeling also allows for the straightforward generation of analytical first-order derivatives. Quantity–quality balance problems are those that involve both...

Two distinct and complementary views, production flow network (PFN) and production flow map (PFM), in context with both the physical and functional aspects of manufacturing are discussed. The systems are helpful for understanding and modeling the production inside complex processes industry facilities. The new views engage the spatial framework or...

The optimization of plant production with the help of nonlinear planning models is highlighted. The planning problems that have been addressed include capacity expansion investments, joint venture allocations, product exchange agreement planning, demand portfolio planning, supply purchase, chemicals and utilities budgeting, and production or operat...

The economic and operability benefits associated with better crude-oil blend scheduling are numerous and significant. The crude-oils that arrive at the oil-refinery to be processed into the various refined-oils must be carefully handled and mixed before they are charged to the atmospheric and vacuum distillation unit or pipestill. The intent of thi...

A review on crude oil blend scheduling optimization was presented. The ability to schedule the crude oil blendshop more effectively provided substantial downstream benefits. The approximately 0.011 maximum excursion from the proxy was expected to be improved by using a 50:50 recipe on the transferline.

This article describes an effective and simple primal heuristic to greedily encourage a reduction in the number of binary or 0–1 logic variables before an implicit enumerative-type search heuristic is deployed to find integer-feasible solutions to ‘hard’ production scheduling problems. The basis of the technique is to employ well-known smoothing fu...

This paper addresses the problem of crude-oil short-term scheduling, which is the first part of the overall refining operations. The problem involves the optimal operation of crude-oil unloading from vessels, its transfer to storage tanks, and the charging schedule for each crude-oil mixture to the distillation units. A novel model is developed bas...

This article presents a simple and effective way to divide the temporal dimension of certain production scheduling optimization problems into smaller, more manageable time chunks which can be solved and brought together to form overall or globally integer-feasible solutions using a depth-first search.

Every day billions of dollars are being spent around the globe on feedstocks, chemicals, utilities, equipment, labor and effluent treatment in order to produce millions of tons of marketable process industry products on-quality and on-time. The purpose of this article is to outline the important formulation details of the optimization models used t...

Two other projection matrices, used in the solution of data reconciliation problems, are described in this short note. The first matrix projection introduced is straightforward to compute, is idempotent and can be easily updated when a measurement is deleted or removed from the problem. The second projection matrix, while although being more numeri...

A new iterative solution to the statistical adjustment of constrained data sets is derived in this paper. The method is general and may be applied to any weighted least squares problem containing nonlinear equality constraints. Other methods are available to solve this class of problem, but are complicated when unmeasured variables and model parame...

Determining the discrete-time proportional plus integral (PI) controller tuning parameters to achieve the smallest possible variance in the manipulated input moves, for a given variance in the controlled output, is the subject of this article. Previous researchers have developed tuning rules for PI and PI permutated nonlinear controllers to achieve...

The focus of this note is to highlight two relatively simple approaches to determine the matrix projection first introduced by Crowe et al. (1983) to solve data reconciliation problems when unmeasured variables exist. The first method uses recursive matrix inversion by partition where the second uses a modified Cholesky factorization. The purpose o...

An optimization-based approach is presented that determines how plant feedstocks should be allocated to storage when there are fewer storage vessels than feedstocks. It is assumed here that material from the storage vessels will be subsequently blended for processing in downstream processes. The objective of the feedstock allocation strategy is cho...

Gasoline production often yields 60-70% of a typical refinery's total revenue. A tight control of blending operations, a key step in gasoline production, therefore provides a crucial edge to the profitability of a refinery. A generalization also known as the pooling problem is used to model many systems with intermediate mixing (or pooling) tanks i...