With the interaction of discrete-event and continuous processes, it is challenging to schedule crude oil operations in a refinery. This paper studies the optimization problem of finding a detailed schedule to realize a given refining schedule. This is a multiobjective optimization problem with a combinatorial nature. Since the original problem cannot be directly solved by using heuristics and meta-heuristics, the problem is transformed into an assignment problem of charging tanks and distillers. Based on such a transformation, by analyzing the properties of the problem, this paper develops a chromosome that can describe a feasible schedule such that meta-heuristics can be applied. Then, it innovatively adopts an improved nondominated sorting genetic algorithm to solve the problem for the first time. An industrial case study is used to test the proposed solution method. The results show that the method makes a significant performance improvement and is applicable to real-life refinery scheduling problems.

Pareto-Optimization for Scheduling of Crude Oil Operations in Refinery via Genetic Algorithm

Tightening piecewise McCormick relaxations for bilinear problems

To effectively operate a refinery and make it competitive, efficient short-term scheduling techniques that utilize commercial software tools for practical applications need to be developed. However, cumbersome details make it difficult to solve the short-term scheduling problem (STSP) of crudeoil operations, and mathematical programming models fail to meet the industrial needs. This article proposes an innovative control-theoretic and formal model-based method to tackle this long-standing issue. This method first models the STSP as a hybrid Petri net (PN) and then derives critically important schedulability conditions. The conditions are used to decompose a complex problem into several tractable subproblems. In each subproblem, there are either continuous variables or discrete variables. For subproblems with continuous variables, this work proposes a linear programming-based method to solve them; while, for subproblems with discrete variables, this work adopts efficient heuristics. Consequently, the STSP is efficiently resolved,and the application of the proposed method is well illustrated via industrial case studies.

Short-Term Scheduling of Crude-Oil Operations: Enhancement of Crude-Oil Operations Scheduling Using a Petri Net-Based Control-Theoretic Approach

Nanofluids have gained significant popularity in the field of sustainable and renewable energy systems. The heat transfer capacity of the working fluid has a huge impact on the efficiency of the renewable energy system. The addition of a small amount of high thermal conductivity solid nanoparticles to a base fluid improves heat transfer. Even though a large amount of research data is available in the literature, some results are contradictory. Many influencing factors, as well as nonlinearity and refutations, make nanofluid research highly challenging and obstruct its potentially valuable uses. On the other hand, data-driven machine learning techniques would be very useful in nanofluid research for forecasting thermophysical features and heat transfer rate, identifying the most influential factors, and assessing the efficiencies of different renewable energy systems. The primary aim of this review study is to look at the features and applications of different machine learning techniques employed in the nanofluid-based renewable energy system, as well as to reveal new developments in machine learning research. A variety of modern machine learning algorithms for nanofluid-based heat transfer studies in renewable and sustainable energy systems are examined, along with their advantages and disadvantages. Artificial neural networks-based model prediction using contemporary commercial software is simple to develop and the most popular. The prognostic capacity may be further improved by combining a marine predator algorithm, genetic algorithm, swarm intelligence optimization, and other intelligent optimization approaches. In addition to the well-known neural networks and fuzzy- and gene-based machine learning techniques, newer ensemble machine learning techniques such as Boosted regression techniques, K-means, K-nearest neighbor (KNN), CatBoost, and XGBoost are gaining popularity due to their improved architectures and adaptabilities to diverse data types. The regularly used neural networks and fuzzy-based algorithms are mostly black-box methods, with the user having little or no understanding of how they function. This is the reason for concern, and ethical artificial intelligence is required. 

Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System

Abstract Torrefaction of biomass under anoxic condition can produce an energy dense and consistent quality solid biomass fuel for combustion and co-firing applications. This paper investigates the fuel characteristics of food waste at torrefaction temperatures ranging from 150 to 600 °C, for torrefaction residence time varying from 0 to 50 min in a horizontal tubular reactor. The torrefied products were characterized in terms of their elemental composition, energy yield, ash content and volatile fraction. The gaseous products from torrefaction of food waste were also analyzed. Thermogravimetric analysis was carried out in order to obtain the kinetic parameters for the torrefaction reaction of food waste. It was found that the food waste undergoes changes in their physical and chemical properties during torrefaction. The energy and mass yield were also found to decrease with an increase in the torrefaction temperature, whereas the higher heating value increased.

A study on torrefaction of food waste

Biodiesel production in micro-reactors: A review

Scheduling of crude oil unloading and loading operations is an important optimization problem for minimizing the significant costs at the front end of a refinery. The scheduling problem involves fi...

Short-Term Scheduling of Refinery Crude Oil Operations

Low temperature steam gasification to produce hydrogen rich gas from kitchen food waste: Influence of steam flow rate and temperature

In this work, the steam gasification of mixed food waste was carried out with torrefaction as pretreatment. Torrefaction was carried out at various temperatures; 230 °C, 260 °C, and 290 °C and for one-hour residence time. Improvement in physico-chemical properties of food waste by torrefaction was studied using proximate, elemental, and compositional analysis. Additionally, the effect of torrefaction on mass yield, energy yield, mass density and energy density was presented. Gasification was then carried out using torrefied food waste as biomass with steam as a gasification agent at 700 °C. The steam flow rate was kept at 0.625 mL/min with a steam to biomass ratio of 1.25. The resulting syngas was characterized for syngas yield, syngas composition, H2 yield, and high heating value (HHV). It was found that syngas yield and H2 field varied from 0.95 to 3.49 m3/Kg and 0.6 to 2.15 m3/Kg respectively from gasification of food waste torrefied at different temperatures. The highest HHV of syngas was found to be 12.19 MJ/Nm3. Cold gas efficiency and carbon conversion efficiency were also determined to evaluate the performance of proposed pretreatment on steam gasification.

Steam gasification with torrefaction as pretreatment to enhance syngas production from mixed food waste

This study evaluated the change in physical and chemical properties of sun-dried food waste, during torrefaction in a fixed bed reactor. Torrefaction was carried out at different temperatures 230 °C, 260 °C, and 290 °C, for different residence times of 15, 30, 45, 60, 75, and 90 min in a fixed bed reactor. The solid torrefied products were analyzed by elemental analysis, proximate analysis, compositional analysis, and thermogravimetric analysis (TGA), and it was found that most suitable residence time for torrefaction was 60 min as torrefaction beyond 60 min did not yield much improvement in changing the properties of kitchen food waste. Carbon content increased from 46.0 to 58.5%, consecutively increasing higher heating value (HHV) from 16.1 to 21.9%. Compositional analysis demonstrated that there was not much hemicellulose left in the raw food waste after cooking; however, it had cellulose in significant proportion which went through decomposition during torrefaction. Lignin was not affected much. Various process parameters such as mass yield, energy yield, and energy density were also studied, and it was found the mass and energy yield decreased by 65.0% and 47.5%, respectively, and energy density increased by 35%. Van Krevelen indicated clearly that torrefied char had achieved coal characteristics.

Sanjeev Yadav

Papers

Biodiesel production in micro-reactors: A review

Short-Term Scheduling of Refinery Crude Oil Operations

Low temperature steam gasification to produce hydrogen rich gas from kitchen food waste: Influence of steam flow rate and temperature

Steam gasification with torrefaction as pretreatment to enhance syngas production from mixed food waste

Evaluation of the physico-chemical development of kitchen food wastes through torrefaction - a biodiversity case study